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Install Dependencies + +```bash +cd OpenViking +uv sync --group benchmark +source .venv/bin/activate +``` + +#### 2. Prepare Datasets + +Use the lightweight datasets in `datasets/Benchmark_Lite/` for quick testing. + +#### 3. Configure LLM + +Edit LLM configuration in `config/*.yaml`. This configuration is used for both: + +- **Answer generation**: Generating answers from retrieved context +- **LLM-as-judge evaluation**: Using LLM to evaluate the quality of generated answers + +#### 4. Configure OpenViking + +If you need to use custom OpenViking configuration (for data ingestion and retrieval), create an `ov.conf` file in the RAGbenchmark directory. This will override the default OpenViking settings. + +You can refer to `examples/ov.conf.example` in the OpenViking root directory for the configuration format. + +#### 5. Run Evaluation + +```bash +cd RAGbenchmark + +# Run complete evaluation (data ingestion, answer generation, evaluation, and data deletion) +python run.py --config config/locomo_config.yaml + +# Only run data ingestion and answer generation stage +python run.py --config config/locomo_config.yaml --step gen + +# Only run evaluation stage (requires generated answers from previous step) +python run.py --config config/locomo_config.yaml --step eval + +# Only run data deletion stage +python run.py --config config/locomo_config.yaml --step delete +``` + +### Supported Datasets + +| Dataset | Type | Docs | QAs | Characteristics | +| -------------- | ---------- | ---- | --- | ---------------------------------------------------------------------------------------------------------------------------- | +| **Locomo** | Multi-turn | 3 | 100 | Real user conversations, 4 question types (factual, temporal, reasoning, understanding) | +| **SyllabusQA** | Syllabus | 8 | 120 | Education domain, 6 question types (single factual, multi factual, single reasoning, multi reasoning, summarization, yes/no) | +| **Qasper** | Academic | 12 | 84 | Research domain, complex reasoning, 3 answer types (extractive, free-form, yes/no) | +| Financebench | Financial | 4 | 13 | Financial domain, SEC financial reports, 3 question types (domain-relevant, metrics-generated, novel-generated) | + +### How to Use Different Datasets + +Each dataset has its own configuration file in the `config/` directory. To use a specific dataset: + +1. **Choose a dataset configuration file**: + - `config/locomo_config.yaml` - For Locomo dataset + - `config/syllabusqa_config.yaml` - For SyllabusQA dataset + - `config/qasper_config.yaml` - For Qasper dataset + - `config/financebench_config.yaml` - For FinanceBench dataset +2. **Run evaluation with the chosen configuration**: + ```bash + # Evaluate with Locomo dataset + python run.py --config config/locomo_config.yaml + + # Evaluate with SyllabusQA dataset + python run.py --config config/syllabusqa_config.yaml + + # Evaluate with Qasper dataset + python run.py --config config/qasper_config.yaml + + # Evaluate with FinanceBench dataset + python run.py --config config/financebench_config.yaml + ``` +3. **Customize configuration (optional)**: + You can copy a dataset configuration file and modify it to suit your needs: + ```bash + cp config/locomo_config.yaml config/my_custom_config.yaml + # Edit config/my_custom_config.yaml with your preferences + python run.py --config config/my_custom_config.yaml + ``` + +### Configuration Guide + +RAGbenchmark uses YAML configuration files to control the evaluation process. Each dataset has its own configuration file in the `config/` directory. + +**Key Configuration Sections:** + +1. **Basic Configuration**: + - `dataset_name`: Name of the dataset being evaluated +2. **Adapter Configuration**: + - `adapter.module`: Python module path for the dataset adapter + - `adapter.class_name`: Class name of the dataset adapter +3. **Execution Configuration**: + - `max_workers`: Number of concurrent worker threads + - `ingest_workers`: Number of worker threads for document ingestion + - `retrieval_topk`: Number of documents to retrieve + - `max_queries`: Limit the number of queries to process (null = all) + - `skip_ingestion`: Skip document ingestion (use existing index) + - `ingest_mode`: Document ingestion mode ("directory" or "per\_file") + - `retrieval_instruction`: Custom instruction for retrieval (empty by default) +4. **Path Configuration**: + - `raw_data`: Path to raw dataset file + - `doc_output_dir`: Directory for processed documents + - `vector_store`: Directory for vector index storage + - `output_dir`: Directory for evaluation results + - `log_file`: Path to log file +5. **LLM Configuration**: + - `llm.model`: LLM model name + - `llm.temperature`: Generation temperature + - `llm.base_url`: API base URL + - `llm.api_key`: API key (keep secure) + +### Evaluation Process Overview + +The evaluation process consists of 5 main stages: + +1. **Data Preparation** + - Convert raw dataset into OpenViking-friendly format + - Process documents for ingestion +2. **Data Ingestion** + - Ingest processed documents into OpenViking vector store + - Create embeddings for documents + - Store vector index for retrieval +3. **Answer Generation** + - For each question, retrieve relevant documents from vector store + - Build prompt with retrieved context and question + - Generate answer using LLM +4. **Evaluation** + - Use LLM-as-judge to evaluate generated answers against gold answers + - Calculate metrics (Recall, F1, Accuracy) +5. **Data Deletion** + - Clean up vector store and remove ingested documents + +### Evaluation Metrics + +- **Recall**: Retrieval recall rate +- **F1 Score**: Answer F1 score +- **Accuracy**: LLM judge score (0-4) +- **Latency**: Retrieval latency +- **Token Usage**: Token consumption + +### Benchmark Results + +Below are the benchmark results across different datasets for reference and comparison. + +#### Performance Metrics Comparison + +```mermaid +--- +config: + xyChart: + plotColorPalette: "#4E79A7, #F28E2B, #E15759" +--- +xychart-beta + title "Performance Metrics by Dataset" + x-axis ["Locomo", "SyllabusQA", "Qasper", "Financebench"] + y-axis "Score (0-1)" 0 --> 1 + bar "Recall" [0.72, 0.73, 0.65, 0.73] + bar "F1 Score" [0.32, 0.34, 0.44, 0.30] + line "Normalized Accuracy" [0.59, 0.74, 0.69, 0.69] +``` + +**Legend:** +- Bar "Recall": Retrieval recall rate +- Bar "F1 Score": Answer F1 score +- Line "Normalized Accuracy": LLM judge score normalized to 0-1 scale + +#### Benchmark Results Table + +| Metric | Locomo | SyllabusQA | Qasper | Financebench | +| ---------------------------- | ------ | ---------- | ------ | ------------ | +| **Recall** | 0.72 | 0.73 | 0.65 | 0.73 | +| **F1 Score** | 0.32 | 0.34 | 0.44 | 0.30 | +| **Accuracy (0-4)** | 2.36 | 2.96 | 2.77 | 2.77 | +| **Normalized Accuracy** | 0.59 | 0.74 | 0.69 | 0.69 | +| **Avg Retrieval Time (s)** | 0.17 | 1.00 | 0.18 | 0.55 | +| **Avg Input Tokens** | 3364 | 2171 | 2160 | 6813 | +| **Avg Output Tokens** | 16 | 69 | 57 | 63 | +| **Total Queries** | 100 | 120 | 79 | 13 | +| **Total Insertion Time (s)** | 132 | 98 | 112 | 456 | + +**Notes:** + +- Results are based on default configuration with `retrieval_topk=5` +- FinanceBench uses `retrieval_topk=10` in this test +- Normalized Accuracy = Accuracy / 4 + +### Output Files + +Evaluation results are saved in the `Output/` directory with the following structure: + +``` +Output/ +└── {dataset_name}/ + └── experiment_{experiment_name}/ + ├── generated_answers.json # Generated answers from LLM + ├── qa_eval_detailed_results.json # Detailed evaluation results + ├── benchmark_metrics_report.json # Aggregated metrics report + ├── docs/ # Processed documents (if skip_ingestion=false) + └── benchmark.log # Log file +``` + +**Vector Store Database Location:** +The vector index (document database) is stored in the path specified by `vector_store` in the configuration file. By default, this is: + +``` +datasets/{dataset_name}/viking_store_index_dir +``` + +#### File descriptions and examples + +**1.** **`benchmark_metrics_report.json`** **- Summary Report** + +- **What it contains**: Aggregated metrics report with overall performance scores + +Example: + +```json +{ + "Insertion Efficiency (Total Dataset)": { + "Total Insertion Time (s)": 131.98, + "Total Input Tokens": 142849, + "Total Output Tokens": 52077, + "Total Embedding Tokens": 95626 + }, + "Query Efficiency (Average Per Query)": { + "Average Retrieval Time (s)": 0.17, + "Average Input Tokens": 3364.46, + "Average Output Tokens": 15.5 + }, + "Dataset": "Locomo", + "Total Queries Evaluated": 100, + "Performance Metrics": { + "Average F1 Score": 0.318, + "Average Recall": 0.724, + "Average Accuracy (Hit 0-4)": 2.36, + "Average Accuracy (normalization)": 0.59 + } +} +``` + +**Field descriptions:** + +- `Insertion Efficiency`: Document ingestion performance statistics +- `Query Efficiency`: Per-query performance averages +- `Performance Metrics`: Core evaluation scores (0-4 scale for Accuracy) + +*** + +**2.** **`generated_answers.json`** **- Generated Answers** + +- **What it contains**: All questions, retrieved contexts, and LLM-generated answers + +Example (single result): + +```json +{ + "_global_index": 0, + "sample_id": "conv-26", + "question": "Would Caroline pursue writing as a career option?", + "gold_answers": ["LIkely no; though she likes reading, she wants to be a counselor"], + "category": "3", + "evidence": ["D7:5", "D7:9"], + "retrieval": { + "latency_sec": 0.288, + "uris": ["viking://resources/...", "viking://resources/..."] + }, + "llm": { + "final_answer": "Not mentioned" + }, + "metrics": { + "Recall": 1.0 + }, + "token_usage": { + "total_input_tokens": 2643, + "llm_output_tokens": 2 + } +} +``` + +**Field descriptions:** + +- `_global_index`: Unique query identifier +- `question`: The question being asked +- `gold_answers`: Ground truth answers +- `retrieval.uris`: URIs of retrieved documents +- `llm.final_answer`: Answer generated by LLM +- `metrics.Recall`: Retrieval recall score (0-1) +- `token_usage`: Token consumption statistics + +*** + +**3.** **`qa_eval_detailed_results.json`** **- Detailed Evaluation** + +- **What it contains**: Per-question evaluation including LLM judge reasoning and scores + +Example (single result): + +```json +{ + "_global_index": 2, + "question": "When did Caroline go to the LGBTQ support group?", + "gold_answers": ["7 May 2023"], + "llm": { + "final_answer": "7 May 2023 (the day before the chat at 1:56 pm on 8 May, 2023)" + }, + "metrics": { + "Recall": 1.0, + "F1": 0.375, + "Accuracy": 4 + }, + "llm_evaluation": { + "prompt_used": "Locomo_0or4", + "reasoning": "The generated answer explicitly includes the exact date 7 May 2023 that matches the gold answer...", + "normalized_score": 4 + } +} +``` + +**Field descriptions:** + +- `metrics.F1`: Answer F1 score (0-1) +- `metrics.Accuracy`: LLM judge score (0-4, 4 = perfect) +- `llm_evaluation.reasoning`: LLM judge's reasoning for the score +- `llm_evaluation.normalized_score`: Final normalized score + +*** + +**4.** **`benchmark.log`** **- Execution Log** + +- **What it contains**: Detailed execution log with timestamps, warnings, and errors +- **How to view**: Open directly in any text editor + +*** + +**5.** **`docs/`** **- Processed Documents** + +- **What it contains**: Processed documents in Markdown format (if `skip_ingestion=false`) +- **How to view**: Open `.md` files directly in any Markdown viewer or text editor + +### Advanced Configuration + +#### Retrieval Instruction Configuration + +You can configure a custom retrieval instruction in the `config.yaml` file to guide the retrieval process. This instruction is prepended to each query during retrieval. + +**Configuration Example:** + +```yaml +# ===========Execution Configuration============ +# Instruction for retrieval, empty by default +# Recommended format: "Target_modality: xxx.\nInstruction:xxx.\nQuery:" +retrieval_instruction: "Target_modality: text.\nInstruction:Locate the part of the conversation where the speakers discuss.\nQuery:" +``` + +**Recommended Format:** + +- `Target_modality: xxx.` - Specify the target modality (e.g., text, image, audio) +- `Instruction: xxx.` - Provide specific instructions for retrieval +- `Query:` - Mark the start of the actual query + +When `retrieval_instruction` is empty, the system will use the raw question for retrieval. + +#### Customizing Prompts + +RAGbenchmark uses dataset-specific and question-type-specific prompts to guide LLM answer generation. You can customize these prompts in the adapter files under `src/adapters/` to improve evaluation results. + +##### Locomo Dataset Prompts (src/adapters/locomo\_adapter.py) + +Locomo has 4 question categories, each with specific instructions: + +- **Category 1 (Factual Extraction)**: + ``` + Extract the exact factual answer from the conversation. + - Use the exact words from the context when possible + - If multiple items, separate with commas + ``` +- **Category 2 (Time-related)**: + ``` + Answer the time-related question. + - Pay close attention to DATE labels in the conversation + - Calculate relative time (e.g., "10 years ago") when needed + - Use the exact dates from the context + ``` +- **Category 3 (Reasoning)**: + ``` + Reason and infer based on the conversation. + - Use ONLY the facts in the context + - State your conclusion clearly (e.g., "Likely yes", "Probably no") + - Do NOT explain your reasoning or provide any basis/justification + - Only output your final conclusion, nothing else + - Do NOT invent information + ``` +- **Category 4 (Understanding/Significance)**: + ``` + Understand the meaning and significance. + - Focus on what the speakers mean, not just what they say + - Identify symbolism or implied meaning + - Use wording from the context when possible + ``` + +##### SyllabusQA Dataset Prompts (src/adapters/syllabusqa\_adapter.py) + +SyllabusQA has 6 question types: + +- **single factual**: Extract single factual answer +- **multi factual**: Extract multiple factual answers +- **single reasoning**: Simple logical reasoning +- **multi reasoning**: Complex reasoning +- **summarization**: Summarize relevant information +- **yes/no**: Yes/No questions + +##### Qasper Dataset Prompts (src/adapters/qasper\_adapter.py) + +Qasper has 3 answer types: + +- **extractive**: Extract exact answer from paper +- **free\_form**: Free-form answer in own words +- **yes\_no**: Yes/No questions + +##### FinanceBench Dataset Prompts (src/adapters/financebench\_adapter.py) + +FinanceBench has 3 question types: + +- **domain-relevant**: Financial domain questions +- **metrics-generated**: Calculate financial metrics +- **novel-generated**: Novel financial questions + +##### How to Customize Prompts + +1. Open the adapter file for your dataset (e.g., `src/adapters/locomo_adapter.py`) +2. Locate the `CATEGORY_INSTRUCTIONS` dictionary +3. Modify the prompt text for the question type(s) you want to improve +4. Re-run the evaluation with the modified prompts + +### Adding New Datasets + +1. Create a new adapter class in `src/adapters/`, inheriting from `BaseAdapter` +2. Create corresponding configuration file in `config/` +3. Implement necessary methods: + - `data_prepare()`: Data preprocessing + - `load_and_transform()`: Load and transform data + - `build_prompt()`: Build prompt + - `post_process_answer()`: Post-process answer + +### Integration with OpenViking + +This project integrates with OpenViking through: + +- Using `openviking` client for data ingestion and retrieval +- Configuring OpenViking connection via `ov.conf` +- Supporting dynamic loading of OpenViking's latest features + +### Frequently Asked Questions (FAQ) + +**Q: How do I skip the data ingestion stage if I already have a vector index?** +A: Set `skip_ingestion: true` in the configuration file. This will use the existing vector index. + +**Q: Can I run only the evaluation stage without re-ingesting documents?** +A: Yes! First run `--step gen` to generate answers, then run `--step eval` to evaluate the generated answers. + +**Q: What should I do if I get an API key error?** +A: Make sure you have set a valid API key in the `llm.api_key` field of your configuration file. Keep your API key secure and do not commit it to version control. + +**Q: How can I limit the number of queries processed for testing?** +A: Set `max_queries` in the configuration file to the number of queries you want to process (e.g., `max_queries: 10`). + +**Q: What's the difference between "directory" and "per\_file" ingest modes?** +A: + +- "directory": Treats the entire directory as one document +- "per\_file": Treats each file as a separate document + +**Q: How do I customize the retrieval instruction?** +A: Set `retrieval_instruction` in the configuration file. The recommended format is: +`"Target_modality: xxx.\nInstruction:xxx.\nQuery:"` + +**Q: Where can I find the evaluation results?** +A: Results are saved in the directory specified by `output_dir` in the configuration file. By default, this is `Output/{dataset_name}/experiment_{experiment_name}/`. + +### License + +Same license as OpenViking. + +*** + +## 中文版本 + +RAGbenchmark 是一个独立的 RAG (Retrieval-Augmented Generation) 系统评测框架，完全兼容最新版本的 OpenViking。 + +### 项目结构 + +``` +RAGbenchmark/ +├── src/ +│ ├── __init__.py +│ ├── pipeline.py # 评测核心管道 +│ ├── adapters/ # 数据集适配器 +│ │ ├── __init__.py +│ │ ├── base.py # 基础适配器类 +│ │ ├── locomo_adapter.py # Locomo 数据集适配器 +│ │ ├── syllabusqa_adapter.py # SyllabusQA 数据集适配器 +│ │ ├── qasper_adapter.py # Qasper 数据集适配器 +│ │ ├── financebench_adapter.py # FinanceBench 数据集适配器 +│ │ └── ... +│ └── core/ # 核心组件 +│ ├── __init__.py +│ ├── logger.py # 日志模块 +│ ├── vector_store.py # 向量存储封装 +│ ├── llm_client.py # LLM 客户端封装 +│ ├── metrics.py # 指标计算 +│ ├── judge_util.py # LLM 裁判工具 +│ └── monitor.py # 监控工具 +├── config/ # 配置文件 +│ ├── config.yaml +│ ├── locomo_config.yaml +│ ├── syllabusqa_config.yaml +│ ├── qasper_config.yaml +│ └── financebench_config.yaml +├── datasets/ # 数据集目录 +│ └── Benchmark_Lite/ # 轻量级评测数据集 +├── Output/ # 输出结果目录 +├── run.py # 主运行脚本 +└── README.md +``` + +### 快速开始 + +#### 1. 安装依赖 + +```bash +cd OpenViking +uv sync --group benchmark +source .venv/bin/activate +``` + +#### 2. 准备数据集 + +使用 `datasets/Benchmark_Lite/` 中的轻量级数据集进行快速测试。 + +#### 3. 配置 LLM + +编辑 `config/*.yaml` 中的 LLM 配置。该配置用于以下两个过程： + +- **答案生成**：根据检索到的上下文生成答案 +- **LLM 裁判评测**：使用 LLM 评估生成答案的质量 + +#### 4. 配置 OpenViking + +如果需要使用自定义的 OpenViking 配置（用于数据入库和检索），请在 RAGbenchmark 目录下创建 `ov.conf` 文件。这将覆盖默认的 OpenViking 设置。 + +你可以参考 OpenViking 根目录下的 `examples/ov.conf.example` 了解配置格式。 + +#### 5. 运行评测 + +```bash +cd RAGbenchmark + +# 运行完整评测流程（数据导入、答案生成、评测和数据删除） +python run.py --config config/locomo_config.yaml + +# 仅运行数据导入和答案生成阶段 +python run.py --config config/locomo_config.yaml --step gen + +# 仅运行评测阶段（需要前面步骤生成的答案） +python run.py --config config/locomo_config.yaml --step eval + +# 仅运行数据删除阶段 +python run.py --config config/locomo_config.yaml --step delete +``` + +### 支持的数据集 + +| 数据集 | 类型 | 文档 | QA数 | 特点 | +| -------------- | ---- | -- | --- | ------------------------------------- | +| **Locomo** | 多轮对话 | 3 | 100 | 真实用户对话，4 种问题类型（事实、时间、推理、理解） | +| **SyllabusQA** | 教学大纲 | 8 | 120 | 教育领域，6 种问题类型（单事实、多事实、单推理、多推理、总结、是/否） | +| **Qasper** | 学术论文 | 12 | 84 | 科研领域，复杂推理，3 种答案类型（提取式、自由式、是/否） | +| Financebench | 财务领域 | 4 | 13 | 财务领域，SEC 财务报告，3 种问题类型（领域相关、指标生成、新颖生成） | + +### 如何使用不同数据集 + +每个数据集在 `config/` 目录下都有自己的配置文件。要使用特定数据集： + +1. **选择数据集配置文件**： + - `config/locomo_config.yaml` - 用于 Locomo 数据集 + - `config/syllabusqa_config.yaml` - 用于 SyllabusQA 数据集 + - `config/qasper_config.yaml` - 用于 Qasper 数据集 + - `config/financebench_config.yaml` - 用于 FinanceBench 数据集 +2. **使用所选配置运行评测**： + ```bash + # 使用 Locomo 数据集评测 + python run.py --config config/locomo_config.yaml + + # 使用 SyllabusQA 数据集评测 + python run.py --config config/syllabusqa_config.yaml + + # 使用 Qasper 数据集评测 + python run.py --config config/qasper_config.yaml + + # 使用 FinanceBench 数据集评测 + python run.py --config config/financebench_config.yaml + ``` +3. **自定义配置（可选）**： + 你可以复制数据集配置文件并根据需要修改： + ```bash + cp config/locomo_config.yaml config/my_custom_config.yaml + # 编辑 config/my_custom_config.yaml 为你的偏好设置 + python run.py --config config/my_custom_config.yaml + ``` + +### 配置指南 + +RAGbenchmark 使用 YAML 配置文件来控制评测过程。每个数据集在 `config/` 目录下都有自己的配置文件。 + +**关键配置部分：** + +1. **基本配置**： + - `dataset_name`：正在评测的数据集名称 +2. **适配器配置**： + - `adapter.module`：数据集适配器的 Python 模块路径 + - `adapter.class_name`：数据集适配器的类名 +3. **执行配置**： + - `max_workers`：并发工作线程数 + - `ingest_workers`：文档入库的工作线程数 + - `retrieval_topk`：要检索的文档数量 + - `max_queries`：限制要处理的查询数量（null = 全部） + - `skip_ingestion`：跳过文档入库（使用现有索引） + - `ingest_mode`：文档入库模式（"directory" 或 "per\_file"） + - `retrieval_instruction`：自定义检索指令（默认为空） +4. **路径配置**： + - `raw_data`：原始数据集文件路径 + - `doc_output_dir`：处理后文档的目录 + - `vector_store`：向量索引存储目录 + - `output_dir`：评测结果目录 + - `log_file`：日志文件路径 +5. **LLM 配置**： + - `llm.model`：LLM 模型名称 + - `llm.temperature`：生成温度 + - `llm.base_url`：API 基础 URL + - `llm.api_key`：API 密钥（请保密） + +### 评测流程概述 + +评测流程包含 5 个主要阶段： + +1. **数据准备** + - 将原始数据集转换为 OpenViking 友好的格式 + - 处理文档以便入库 +2. **数据入库** + - 将处理后的文档入库到 OpenViking 向量存储 + - 为文档创建嵌入向量 + - 存储向量索引以便检索 +3. **答案生成** + - 对每个问题，从向量存储中检索相关文档 + - 使用检索到的上下文和问题构建提示词 + - 使用 LLM 生成答案 +4. **评测** + - 使用 LLM 裁判将生成的答案与标准答案进行对比评估 + - 计算指标（Recall、F1、Accuracy） +5. **数据删除** + - 清理向量存储并删除已入库的文档 + +### 评测指标 + +- **Recall**: 检索召回率 +- **F1 Score**: 答案 F1 值 +- **Accuracy**: LLM 裁判评分 (0-4) +- **Latency**: 检索延迟 +- **Token Usage**: Token 使用量 + +### 评测结果 + +以下是不同数据集的评测结果，供参考和对比。 + +#### 性能指标对比 + +```mermaid +xychart-beta + title "各数据集性能指标" + x-axis ["Locomo", "SyllabusQA", "Qasper", "Financebench"] + y-axis "分数 (0-1)" 0 --> 1 + bar "召回率" [0.72, 0.73, 0.65, 0.73] + bar "F1 分数" [0.32, 0.34, 0.44, 0.30] + line "归一化准确率" [0.59, 0.74, 0.69, 0.69] +``` + +**图例：** +- 柱状图 "召回率"：检索召回率 +- 柱状图 "F1 分数"：答案 F1 值 +- 线条图 "归一化准确率"：LLM 裁判评分归一化到 0-1 范围 + +#### 评测结果表 + +| 指标 | Locomo | SyllabusQA | Qasper | Financebench | +| -------------------------------- | ------ | ---------- | ------ | ------------ | +| **召回率 (Recall)** | 0.72 | 0.73 | 0.65 | 0.73 | +| **F1 分数 (F1 Score)** | 0.32 | 0.34 | 0.44 | 0.30 | +| **准确率 (Accuracy 0-4)** | 2.36 | 2.96 | 2.77 | 2.77 | +| **归一化准确率 (Normalized Accuracy)** | 0.59 | 0.74 | 0.69 | 0.69 | +| **平均检索时间 (秒)** | 0.17 | 1.00 | 0.18 | 0.55 | +| **平均输入 Token 数** | 3364 | 2171 | 2160 | 6813 | +| **平均输出 Token 数** | 16 | 69 | 57 | 63 | +| **总查询数** | 100 | 120 | 79 | 13 | +| **总入库时间 (秒)** | 132 | 98 | 112 | 456 | + +**说明：** + +- 结果基于默认配置，`retrieval_topk=5`，FinanceBench 在此测试中使用 `retrieval_topk=10` +- 归一化准确率 = 准确率 / 4 + +### 输出文件 + +评测结果保存在 `Output/` 目录中，结构如下： + +``` +Output/ +└── {dataset_name}/ + └── experiment_{experiment_name}/ + ├── generated_answers.json # LLM 生成的答案 + ├── qa_eval_detailed_results.json # 详细的评测结果 + ├── benchmark_metrics_report.json # 汇总的指标报告 + └── benchmark.log # 日志文件 +``` + +**向量存储数据库位置：** +向量索引（文档数据库）存储在配置文件中 `vector_store` 指定的路径。默认情况下为： + +``` +datasets/{dataset_name}/viking_store_index_dir +``` + +#### 文件说明及示例 + +**1.** **`benchmark_metrics_report.json`** **- 汇总报告** + +- **包含内容**: 汇总的指标报告，包含整体性能分数 + +示例： + +```json +{ + "Insertion Efficiency (Total Dataset)": { + "Total Insertion Time (s)": 131.98, + "Total Input Tokens": 142849, + "Total Output Tokens": 52077, + "Total Embedding Tokens": 95626 + }, + "Query Efficiency (Average Per Query)": { + "Average Retrieval Time (s)": 0.17, + "Average Input Tokens": 3364.46, + "Average Output Tokens": 15.5 + }, + "Dataset": "Locomo", + "Total Queries Evaluated": 100, + "Performance Metrics": { + "Average F1 Score": 0.318, + "Average Recall": 0.724, + "Average Accuracy (Hit 0-4)": 2.36, + "Average Accuracy (normalization)": 0.59 + } +} +``` + +**字段说明：** + +- `Insertion Efficiency`: 文档入库性能统计 +- `Query Efficiency`: 每个查询的平均性能 +- `Performance Metrics`: 核心评测分数（Accuracy 为 0-4 分） + +*** + +**2.** **`generated_answers.json`** **- 生成的答案** + +- **包含内容**: 所有问题、检索到的上下文和 LLM 生成的答案 + +示例（单个结果）： + +```json +{ + "_global_index": 0, + "sample_id": "conv-26", + "question": "Would Caroline pursue writing as a career option?", + "gold_answers": ["LIkely no; though she likes reading, she wants to be a counselor"], + "category": "3", + "evidence": ["D7:5", "D7:9"], + "retrieval": { + "latency_sec": 0.288, + "uris": ["viking://resources/...", "viking://resources/..."] + }, + "llm": { + "final_answer": "Not mentioned" + }, + "metrics": { + "Recall": 1.0 + }, + "token_usage": { + "total_input_tokens": 2643, + "llm_output_tokens": 2 + } +} +``` + +**字段说明：** + +- `_global_index`: 唯一的查询标识符 +- `question`: 被询问的问题 +- `gold_answers`: 标准答案 +- `retrieval.uris`: 检索到的文档的 URI +- `llm.final_answer`: LLM 生成的答案 +- `metrics.Recall`: 检索召回分数 (0-1) +- `token_usage`: Token 消耗统计 + +*** + +**3.** **`qa_eval_detailed_results.json`** **- 详细评测结果** + +- **包含内容**: 每个问题的评测，包括 LLM 裁判推理和分数 + +示例（单个结果）： + +```json +{ + "_global_index": 2, + "question": "When did Caroline go to the LGBTQ support group?", + "gold_answers": ["7 May 2023"], + "llm": { + "final_answer": "7 May 2023 (the day before the chat at 1:56 pm on 8 May, 2023)" + }, + "metrics": { + "Recall": 1.0, + "F1": 0.375, + "Accuracy": 4 + }, + "llm_evaluation": { + "prompt_used": "Locomo_0or4", + "reasoning": "The generated answer explicitly includes the exact date 7 May 2023 that matches the gold answer...", + "normalized_score": 4 + } +} +``` + +**字段说明：** + +- `metrics.F1`: 答案 F1 分数 (0-1) +- `metrics.Accuracy`: LLM 裁判分数 (0-4, 4 = 满分) +- `llm_evaluation.reasoning`: LLM 裁判给出分数的推理 +- `llm_evaluation.normalized_score`: 最终归一化分数 + +*** + +**4.** **`benchmark.log`** **- 执行日志** + +- **包含内容**: 详细的执行日志，包含时间戳、警告和错误 +- **查看方法**: 直接在任意文本编辑器中打开 + +*** + +**5.** **`docs/`** **- 处理后的文档** + +- **包含内容**: Markdown 格式的处理后文档（如果 `skip_ingestion=false`） +- **查看方法**: 直接在任意 Markdown 查看器或文本编辑器中打开 `.md` 文件 + +### 高级配置 + +#### 检索指令配置 + +你可以在 `config.yaml` 文件中配置自定义的检索指令，以指导检索过程。该指令会在检索时被添加到每个查询的前面。 + +**配置示例：** + +```yaml +# ===========Execution Configuration============ +# 检索指令，默认为空 +# 推荐格式："Target_modality: xxx.\nInstruction:xxx.\nQuery:" +retrieval_instruction: "Target_modality: text.\nInstruction:Locate the part of the conversation where the speakers discuss.\nQuery:" +``` + +**推荐格式：** + +- `Target_modality: xxx.` - 指定目标模态（例如，文本、图像、音频） +- `Instruction: xxx.` - 提供特定的检索指令 +- `Query:` - 标记实际查询的开始 + +当 `retrieval_instruction` 为空时，系统将使用原始问题进行检索。 + +#### 自定义 Prompt + +RAGbenchmark 使用数据集特定和问题类型特定的 prompt 来指导 LLM 生成答案。你可以在 `src/adapters/` 下的适配器文件中自定义这些 prompt，以改进评测结果。 + +##### Locomo 数据集 Prompt (src/adapters/locomo\_adapter.py) + +Locomo 有 4 种问题类别，每种都有特定的指令： + +- **类别 1（事实提取）**: + ``` + Extract the exact factual answer from the conversation. + - Use the exact words from the context when possible + - If multiple items, separate with commas + ``` +- **类别 2（时间相关）**: + ``` + Answer the time-related question. + - Pay close attention to DATE labels in the conversation + - Calculate relative time (e.g., "10 years ago") when needed + - Use the exact dates from the context + ``` +- **类别 3（推理）**: + ``` + Reason and infer based on the conversation. + - Use ONLY the facts in the context + - State your conclusion clearly (e.g., "Likely yes", "Probably no") + - Do NOT explain your reasoning or provide any basis/justification + - Only output your final conclusion, nothing else + - Do NOT invent information + ``` +- **类别 4（理解/意义）**: + ``` + Understand the meaning and significance. + - Focus on what the speakers mean, not just what they say + - Identify symbolism or implied meaning + - Use wording from the context when possible + ``` + +##### SyllabusQA 数据集 Prompt (src/adapters/syllabusqa\_adapter.py) + +SyllabusQA 有 6 种问题类型： + +- **single factual**: 提取单个事实答案 +- **multi factual**: 提取多个事实答案 +- **single reasoning**: 简单逻辑推理 +- **multi reasoning**: 复杂推理 +- **summarization**: 总结相关信息 +- **yes/no**: 是/否问题 + +##### Qasper 数据集 Prompt (src/adapters/qasper\_adapter.py) + +Qasper 有 3 种答案类型： + +- **extractive**: 从论文中提取精确答案 +- **free\_form**: 用自己的话自由回答 +- **yes\_no**: 是/否问题 + +##### FinanceBench 数据集 Prompt (src/adapters/financebench\_adapter.py) + +FinanceBench 有 3 种问题类型： + +- **domain-relevant**: 财务领域问题 +- **metrics-generated**: 计算财务指标 +- **novel-generated**: 新颖的财务问题 + +##### 如何自定义 Prompt + +1. 打开你的数据集的适配器文件（例如 `src/adapters/locomo_adapter.py`） +2. 找到 `CATEGORY_INSTRUCTIONS` 字典 +3. 修改你想要改进的问题类型的 prompt 文本 +4. 使用修改后的 prompt 重新运行评测 + +### 添加新数据集 + +1. 在 `src/adapters/` 中创建新的适配器类，继承自 `BaseAdapter` +2. 在 `config/` 中创建对应的配置文件 +3. 实现必要的方法： + - `data_prepare()`: 数据预处理 + - `load_and_transform()`: 加载并转换数据 + - `build_prompt()`: 构建提示词 + - `post_process_answer()`: 后处理答案 + +### 与 OpenViking 集成 + +本项目通过以下方式与 OpenViking 集成： + +- 使用 `openviking` 客户端进行数据入库和检索 +- 通过 `ov.conf` 配置 OpenViking 连接 +- 支持动态加载 OpenViking 的最新功能 + +### 常见问题解答（FAQ） + +**Q：如果我已经有向量索引，如何跳过数据入库阶段？** +A：在配置文件中设置 `skip_ingestion: true`。这将使用现有的向量索引。 + +**Q：我可以只运行评测阶段而不重新入库文档吗？** +A：可以！首先运行 `--step gen` 生成答案，然后运行 `--step eval` 来评测生成的答案。 + +**Q：如果我遇到 API 密钥错误，应该怎么办？** +A：确保你在配置文件的 `llm.api_key` 字段中设置了有效的 API 密钥。请保管好你的 API 密钥，不要将其提交到版本控制中。 + +**Q：如何限制处理的查询数量以进行测试？** +A：在配置文件中设置 `max_queries` 为你想要处理的查询数量（例如，`max_queries: 10`）。 + +**Q："directory" 和 "per\_file" 入库模式有什么区别？** +A： + +- "directory"：将整个目录视为一个文档 +- "per\_file"：将每个文件视为一个单独的文档 + +**Q：如何自定义检索指令？** +A：在配置文件中设置 `retrieval_instruction`。推荐格式为： +`"Target_modality: xxx.\nInstruction:xxx.\nQuery:"` + +**Q：我可以在哪里找到评测结果？** +A：结果保存在配置文件中 `output_dir` 指定的目录中。默认情况下，这是 `Output/{dataset_name}/experiment_{experiment_name}/`。 + +### License + +与 OpenViking 使用相同的许可证。 diff --git a/RAGbenchmark/config/config.yaml b/RAGbenchmark/config/config.yaml new file mode 100644 index 00000000..7191f66b --- /dev/null +++ b/RAGbenchmark/config/config.yaml @@ -0,0 +1,59 @@ +# RAG Benchmark Configuration File +project_name: "RAG_Benchmark" + +# ===========Basic Configuration============ +# Dataset name to identify the current dataset being evaluated +# Supported datasets: NarrativeQA, Qasper, Locomo, SyllabusQA, etc. +dataset_name: "SyllabusQA" + +# ===========Adapter Configuration============ +# Dataset adapter configuration for loading and processing dataset-specific formats +adapter: + # Adapter module path (Python import path) + module: "src.adapters.syllabusqa_adapter" + # Adapter class name + class_name: "SyllabusQAAdapter" + +# ===========Execution Configuration============ +# Maximum number of concurrent worker threads for parallel QA task processing +max_workers: 10 +# Number of concurrent worker threads for document ingestion +ingest_workers: 10 +# Number of documents to retrieve (top-k) +retrieval_topk: 5 +# Limit the maximum number of queries to process, null means process all queries +max_queries: null +# Whether to skip document ingestion phase (true means use existing vector index) +skip_ingestion: false +# Document ingestion mode +# - "directory": Unified directory ingestion mode, treats the entire directory as one document +# - "per_file": Per-file ingestion mode, each file is treated as a separate document +ingest_mode: "per_file" +# Instruction for retrieval, empty by default +retrieval_instruction: "" + +# ===========Path Configuration============ +# All paths support {dataset_name} placeholder, which will be automatically replaced with the current dataset name +paths: + # Raw data file path + raw_data: "datasets/Benchmark_Lite/{dataset_name}/test.csv" + # Output directory for processed documents + doc_output_dir: "datasets/{dataset_name}/{dataset_name}_processed_docs" + # Vector index storage directory + vector_store: "datasets/{dataset_name}/viking_store_index_dir" + # Results output directory + output_dir: "Output/{dataset_name}/experiment_dev" + # Log file path + log_file: "Output/{dataset_name}/experiment_dev/benchmark.log" + +# ===========LLM Configuration============ +# Large language model configuration for answer generation and evaluation +llm: + # Model name + model: "doubao-seed-2-0-pro-260215" + # Generation temperature (0 means deterministic output, higher values mean more randomness) + temperature: 0 + # API base URL + base_url: "https://ark.cn-beijing.volces.com/api/v3" + # API key (keep this secure, do not commit to code repositories) + api_key: "" diff --git a/RAGbenchmark/config/financebench_config.yaml b/RAGbenchmark/config/financebench_config.yaml new file mode 100644 index 00000000..7a41fad5 --- /dev/null +++ b/RAGbenchmark/config/financebench_config.yaml @@ -0,0 +1,32 @@ +project_name: "RAG_Benchmark" + +# ===========Modify Configuration================ +dataset_name: "Financebench" + +adapter: + module: "src.adapters.financebench_adapter" + class_name: "FinanceBenchAdapter" + +execution: + max_workers: 8 + ingest_workers: 8 + retrieval_topk: 10 + max_queries: null + skip_ingestion: false + ingest_mode: "directory" + retrieval_instruction: "" +# ================================== + +paths: + raw_data: "datasets/Benchmark_Lite/{dataset_name}/financebench_subset.jsonl" + doc_output_dir: "datasets/{dataset_name}/{dataset_name}_processed_docs" + vector_store: "datasets/{dataset_name}/{dataset_name}_viking_store_index" + output_dir: "Output/{dataset_name}/experiment_test_top_{retrieval_topk}" + log_file: "Output/{dataset_name}/experiment_test_top_{retrieval_topk}/benchmark.log" + + +llm: + model: "doubao-seed-2-0-pro-260215" + temperature: 0 + base_url: "https://ark.cn-beijing.volces.com/api/v3" + api_key: "" diff --git a/RAGbenchmark/config/locomo_config.yaml b/RAGbenchmark/config/locomo_config.yaml new file mode 100644 index 00000000..254282b2 --- /dev/null +++ b/RAGbenchmark/config/locomo_config.yaml @@ -0,0 +1,32 @@ +project_name: "RAG_Benchmark" + +# ===========Modify Configuration================ +dataset_name: "Locomo" + +adapter: + module: "src.adapters.locomo_adapter" + class_name: "LocomoAdapter" + +execution: + max_workers: 8 + ingest_workers: 8 + retrieval_topk: 5 + max_queries: 20 + skip_ingestion: false + ingest_mode: "directory" + retrieval_instruction: "" +# ================================== + +paths: + raw_data: "datasets/Benchmark_Lite/{dataset_name}/Locomo.json" + doc_output_dir: "datasets/{dataset_name}/{dataset_name}_processed_docs" + vector_store: "datasets/{dataset_name}/{dataset_name}_viking_store_index_dir" + output_dir: "Output/{dataset_name}/experiment_test_top_{retrieval_topk}" + log_file: "Output/{dataset_name}/experiment_test_top_{retrieval_topk}/benchmark.log" + + +llm: + model: "doubao-seed-2-0-pro-260215" + temperature: 0 + base_url: "https://ark.cn-beijing.volces.com/api/v3" + api_key: "" diff --git a/RAGbenchmark/config/qasper_config.yaml b/RAGbenchmark/config/qasper_config.yaml new file mode 100644 index 00000000..e7e90f13 --- /dev/null +++ b/RAGbenchmark/config/qasper_config.yaml @@ -0,0 +1,32 @@ +project_name: "RAG_Benchmark" + +# ===========Modify Configuration================ +dataset_name: "Qasper" + +adapter: + module: "src.adapters.qasper_adapter" + class_name: "QasperAdapter" + +execution: + max_workers: 8 + ingest_workers: 8 + retrieval_topk: 5 + max_queries: 10 + skip_ingestion: false + ingest_mode: "directory" + retrieval_instruction: "" +# ================================== + +paths: + raw_data: "datasets/Benchmark_Lite/{dataset_name}/qasper-dev-v0.3.json" + doc_output_dir: "datasets/{dataset_name}/{dataset_name}_processed_docs" + vector_store: "datasets/{dataset_name}/{dataset_name}_viking_store_index" + output_dir: "Output/{dataset_name}/experiment_test_top_{retrieval_topk}" + log_file: "Output/{dataset_name}/experiment_test_top_{retrieval_topk}/benchmark.log" + + +llm: + model: "doubao-seed-2-0-pro-260215" + temperature: 0 + base_url: "https://ark.cn-beijing.volces.com/api/v3" + api_key: "" diff --git a/RAGbenchmark/config/syllabusqa_config.yaml b/RAGbenchmark/config/syllabusqa_config.yaml new file mode 100644 index 00000000..26bd86d2 --- /dev/null +++ b/RAGbenchmark/config/syllabusqa_config.yaml @@ -0,0 +1,32 @@ +project_name: "RAG_Benchmark" + +# ===========Modify Configuration================ +dataset_name: "SyllabusQA" + +adapter: + module: "src.adapters.syllabusqa_adapter" + class_name: "SyllabusQAAdapter" + +execution: + max_workers: 8 + ingest_workers: 8 + retrieval_topk: 5 + max_queries: 20 + skip_ingestion: false + ingest_mode: "directory" + retrieval_instruction: "" +# ================================== + +paths: + raw_data: "datasets/Benchmark_Lite/{dataset_name}/test.csv" + doc_output_dir: "datasets/{dataset_name}/{dataset_name}_processed_docs" + vector_store: "datasets/{dataset_name}/{dataset_name}_viking_store_index" + output_dir: "Output/{dataset_name}/experiment_test_top_{retrieval_topk}_111" + log_file: "Output/{dataset_name}/experiment_test_top_{retrieval_topk}_111/benchmark.log" + + +llm: + model: "doubao-seed-2-0-pro-260215" + temperature: 0 + base_url: "https://ark.cn-beijing.volces.com/api/v3" + api_key: "" diff --git a/RAGbenchmark/datasets/Benchmark_Lite/Financebench/financebench_subset.jsonl b/RAGbenchmark/datasets/Benchmark_Lite/Financebench/financebench_subset.jsonl new file mode 100644 index 00000000..5b547fe3 --- /dev/null +++ b/RAGbenchmark/datasets/Benchmark_Lite/Financebench/financebench_subset.jsonl @@ -0,0 +1,13 @@ +{"financebench_id": "financebench_id_01319", "company": "AES Corporation", "doc_name": "AES_2022_10K", "question_type": "domain-relevant", "question_reasoning": "Information extraction", "domain_question_num": "dg21", "question": "What is the quantity of restructuring costs directly outlined in AES Corporation's income statements for FY2022? If restructuring costs are not explicitly outlined then state 0.", "answer": "0", "justification": null, "dataset_subset_label": "OPEN_SOURCE", "evidence": [{"evidence_text": "Consolidated Statements of Operations\nYears ended December 31, 2022, 2021, and 2020\n2022\n2021\n2020\n(in millions, except per share amounts)\nRevenue:\nRegulated\n$\n3,538 \n$\n2,868 \n$\n2,661 \nNon-Regulated\n9,079 \n8,273 \n6,999 \nTotal revenue\n12,617 \n11,141 \n9,660 \nCost of Sales:\nRegulated\n(3,162)\n(2,448)\n(2,235)\nNon-Regulated\n(6,907)\n(5,982)\n(4,732)\nTotal cost of sales\n(10,069)\n(8,430)\n(6,967)\nOperating margin\n2,548 \n2,711 \n2,693 \nGeneral and administrative expenses\n(207)\n(166)\n(165)\nInterest expense\n(1,117)\n(911)\n(1,038)\nInterest income\n389 \n298 \n268 \nLoss on extinguishment of debt\n(15)\n(78)\n(186)\nOther expense\n(68)\n(60)\n(53)\nOther income\n102 \n410 \n75 \nLoss on disposal and sale of business interests\n(9)\n(1,683)\n(95)\nGoodwill impairment expense\n(777)\n \n \nAsset impairment expense\n(763)\n(1,575)\n(864)\nForeign currency transaction gains (losses)\n(77)\n(10)\n55 \nOther non-operating expense\n(175)\n \n(202)\nINCOME (LOSS) FROM CONTINUING OPERATIONS BEFORE TAXES AND EQUITY IN EARNINGS OF AFFILIATES\n(169)\n(1,064)\n488 \nIncome tax benefit (expense)\n(265)\n133 \n(216)\nNet equity in losses of affiliates\n(71)\n(24)\n(123)\nINCOME (LOSS) FROM CONTINUING OPERATIONS\n(505)\n(955)\n149 \nGain from disposal of discontinued businesses, net of income tax expense of $0, $1, and $0, respectively\n \n4 \n3 \nNET INCOME (LOSS)\n(505)\n(951)\n152 \nLess: Net loss (income) attributable to noncontrolling interests and redeemable stock of subsidiaries\n(41)\n542 \n(106)\nNET INCOME (LOSS) ATTRIBUTABLE TO THE AES CORPORATION\n$\n(546)\n$\n(409)\n$\n46", "doc_name": "AES_2022_10K", "evidence_page_num": 131, "evidence_text_full_page": "129 \nConsolidated Statements of Operations\nYears ended December 31, 2022, 2021, and 2020\n2022\n2021\n2020\n(in millions, except per share amounts)\nRevenue:\nRegulated\n$\n3,538 \n$\n2,868 \n$\n2,661 \nNon-Regulated\n9,079 \n8,273 \n6,999 \nTotal revenue\n12,617 \n11,141 \n9,660 \nCost of Sales:\nRegulated\n(3,162)\n(2,448)\n(2,235)\nNon-Regulated\n(6,907)\n(5,982)\n(4,732)\nTotal cost of sales\n(10,069)\n(8,430)\n(6,967)\nOperating margin\n2,548 \n2,711 \n2,693 \nGeneral and administrative expenses\n(207)\n(166)\n(165)\nInterest expense\n(1,117)\n(911)\n(1,038)\nInterest income\n389 \n298 \n268 \nLoss on extinguishment of debt\n(15)\n(78)\n(186)\nOther expense\n(68)\n(60)\n(53)\nOther income\n102 \n410 \n75 \nLoss on disposal and sale of business interests\n(9)\n(1,683)\n(95)\nGoodwill impairment expense\n(777)\n \n \nAsset impairment expense\n(763)\n(1,575)\n(864)\nForeign currency transaction gains (losses)\n(77)\n(10)\n55 \nOther non-operating expense\n(175)\n \n(202)\nINCOME (LOSS) FROM CONTINUING OPERATIONS BEFORE TAXES AND EQUITY IN EARNINGS OF AFFILIATES\n(169)\n(1,064)\n488 \nIncome tax benefit (expense)\n(265)\n133 \n(216)\nNet equity in losses of affiliates\n(71)\n(24)\n(123)\nINCOME (LOSS) FROM CONTINUING OPERATIONS\n(505)\n(955)\n149 \nGain from disposal of discontinued businesses, net of income tax expense of $0, $1, and $0, respectively\n \n4 \n3 \nNET INCOME (LOSS)\n(505)\n(951)\n152 \nLess: Net loss (income) attributable to noncontrolling interests and redeemable stock of subsidiaries\n(41)\n542 \n(106)\nNET INCOME (LOSS) ATTRIBUTABLE TO THE AES CORPORATION\n$\n(546)\n$\n(409)\n$\n46 \nAMOUNTS ATTRIBUTABLE TO THE AES CORPORATION COMMON STOCKHOLDERS:\nIncome (loss) from continuing operations, net of tax\n$\n(546)\n$\n(413)\n$\n43 \nIncome from discontinued operations, net of tax\n \n4 \n3 \nNET INCOME (LOSS) ATTRIBUTABLE TO THE AES CORPORATION\n$\n(546)\n$\n(409)\n$\n46 \nBASIC EARNINGS PER SHARE:\nIncome (loss) from continuing operations attributable to The AES Corporation common stockholders, net of tax\n$\n(0.82)\n$\n(0.62)\n$\n0.06 \nIncome from discontinued operations attributable to The AES Corporation common stockholders, net of tax\n \n0.01 \n0.01 \nNET INCOME (LOSS) ATTRIBUTABLE TO THE AES CORPORATION COMMON STOCKHOLDERS\n$\n(0.82)\n$\n(0.61)\n$\n0.07 \nDILUTED EARNINGS PER SHARE:\nIncome (loss) from continuing operations attributable to The AES Corporation common stockholders, net of tax\n$\n(0.82)\n$\n(0.62)\n$\n0.06 \nIncome from discontinued operations attributable to The AES Corporation common stockholders, net of tax\n \n0.01 \n0.01 \nNET INCOME (LOSS) ATTRIBUTABLE TO THE AES CORPORATION COMMON STOCKHOLDERS\n$\n(0.82)\n$\n(0.61)\n$\n0.07 \nSee Accompanying Notes to Consolidated Financial Statements.\n"}]} +{"financebench_id": "financebench_id_00540", "company": "AES Corporation", "doc_name": "AES_2022_10K", "question_type": "domain-relevant", "question_reasoning": "Numerical reasoning OR Logical reasoning", "domain_question_num": "dg25", "question": "Roughly how many times has AES Corporation sold its inventory in FY2022? Calculate inventory turnover ratio for the FY2022; if conventional inventory management is not meaningful for the company then state that and explain why.", "answer": "AES has converted inventory 9.5 times in FY 2022.", "justification": "Cost of sales/Inventory\n10069/1055", "dataset_subset_label": "OPEN_SOURCE", "evidence": [{"evidence_text": "Consolidated Balance Sheets\nDecember 31, 2022 and 2021\n2022\n2021\n(in millions, except share and per share data)\nASSETS\nCURRENT ASSETS\nCash and cash equivalents\n$\n1,374 \n$\n943 \nRestricted cash\n536 \n304 \nShort-term investments\n730 \n232 \nAccounts receivable, net of allowance for doubtful accounts of $5 and $5, respectively\n1,799 \n1,418 \nInventory\n1,055 \n604", "doc_name": "AES_2022_10K", "evidence_page_num": 129, "evidence_text_full_page": "128 \nConsolidated Balance Sheets\nDecember 31, 2022 and 2021\n2022\n2021\n(in millions, except share and per share data)\nASSETS\nCURRENT ASSETS\nCash and cash equivalents\n$\n1,374 \n$\n943 \nRestricted cash\n536 \n304 \nShort-term investments\n730 \n232 \nAccounts receivable, net of allowance for doubtful accounts of $5 and $5, respectively\n1,799 \n1,418 \nInventory\n1,055 \n604 \nPrepaid expenses\n98 \n142 \nOther current assets, net of CECL allowance of $2 and $0, respectively\n1,533 \n897 \nCurrent held-for-sale assets\n518 \n816 \nTotal current assets\n7,643 \n5,356 \nNONCURRENT ASSETS\nProperty, Plant and Equipment:\nLand\n470 \n426 \nElectric generation, distribution assets and other\n26,599 \n25,552 \nAccumulated depreciation\n(8,651)\n(8,486)\nConstruction in progress\n4,621 \n2,414 \nProperty, plant and equipment, net\n23,039 \n19,906 \nOther Assets:\nInvestments in and advances to affiliates\n952 \n1,080 \nDebt service reserves and other deposits\n177 \n237 \nGoodwill\n362 \n1,177 \nOther intangible assets, net of accumulated amortization of $434 and $385, respectively\n1,841 \n1,450 \nDeferred income taxes\n319 \n409 \nLoan receivable, net of allowance of $26\n1,051 \n \nOther noncurrent assets, net of allowance of $51 and $23, respectively\n2,979 \n2,188 \nNoncurrent held-for-sale assets\n \n1,160 \nTotal other assets\n7,681 \n7,701 \nTOTAL ASSETS\n$\n38,363 \n$\n32,963 \nLIABILITIES AND EQUITY\nCURRENT LIABILITIES\nAccounts payable\n$\n1,730 \n$\n1,153 \nAccrued interest\n249 \n182 \nAccrued non-income taxes\n249 \n266 \nAccrued and other liabilities\n2,151 \n1,205 \nNon-recourse debt, including $416 and $302, respectively, related to variable interest entities\n1,758 \n1,367 \nCurrent held-for-sale liabilities\n354 \n559 \nTotal current liabilities\n6,491 \n4,732 \nNONCURRENT LIABILITIES\nRecourse debt\n3,894 \n3,729 \nNon-recourse debt, including $2,295 and $2,223, respectively, related to variable interest entities\n17,846 \n13,603 \nDeferred income taxes\n1,139 \n977 \nOther noncurrent liabilities\n3,168 \n3,358 \nNoncurrent held-for-sale liabilities\n \n740 \nTotal noncurrent liabilities\n26,047 \n22,407 \nCommitments and Contingencies (see Notes 12 and 13)\nRedeemable stock of subsidiaries\n1,321 \n1,257 \nEQUITY\nTHE AES CORPORATION STOCKHOLDERS EQUITY\nPreferred stock (without par value, 50,000,000 shares authorized; 1,043,050 issued and outstanding at December 31, 2022 and\nDecember 31, 2021)\n838 \n838 \nCommon stock ($0.01 par value, 1,200,000,000 shares authorized; 818,790,001 issued and 668,743,464 outstanding at December\n31, 2022 and 818,717,043 issued and 666,793,625 outstanding at December 31, 2021)\n8 \n8 \nAdditional paid-in capital\n6,688 \n7,106 \nAccumulated deficit\n(1,635)\n(1,089)\nAccumulated other comprehensive loss\n(1,640)\n(2,220)\nTreasury stock, at cost (150,046,537 and 151,923,418 shares at December 31, 2022 and December 31, 2021, respectively)\n(1,822)\n(1,845)\nTotal AES Corporation stockholders equity\n2,437 \n2,798 \nNONCONTROLLING INTERESTS\n2,067 \n1,769 \nTotal equity\n4,504 \n4,567 \nTOTAL LIABILITIES AND EQUITY\n$\n38,363 \n$\n32,963 \nSee Accompanying Notes to Consolidated Financial Statements.\n"}, {"evidence_text": "Consolidated Statements of Operations\nYears ended December 31, 2022, 2021, and 2020\n2022\n2021\n2020\n(in millions, except per share amounts)\nRevenue:\nRegulated\n$\n3,538 \n$\n2,868 \n$\n2,661 \nNon-Regulated\n9,079 \n8,273 \n6,999 \nTotal revenue\n12,617 \n11,141 \n9,660 \nCost of Sales:\nRegulated\n(3,162)\n(2,448)\n(2,235)\nNon-Regulated\n(6,907)\n(5,982)\n(4,732)\nTotal cost of sales\n(10,069)\n(8,430)\n(6,967)", "doc_name": "AES_2022_10K", "evidence_page_num": 131, "evidence_text_full_page": "129 \nConsolidated Statements of Operations\nYears ended December 31, 2022, 2021, and 2020\n2022\n2021\n2020\n(in millions, except per share amounts)\nRevenue:\nRegulated\n$\n3,538 \n$\n2,868 \n$\n2,661 \nNon-Regulated\n9,079 \n8,273 \n6,999 \nTotal revenue\n12,617 \n11,141 \n9,660 \nCost of Sales:\nRegulated\n(3,162)\n(2,448)\n(2,235)\nNon-Regulated\n(6,907)\n(5,982)\n(4,732)\nTotal cost of sales\n(10,069)\n(8,430)\n(6,967)\nOperating margin\n2,548 \n2,711 \n2,693 \nGeneral and administrative expenses\n(207)\n(166)\n(165)\nInterest expense\n(1,117)\n(911)\n(1,038)\nInterest income\n389 \n298 \n268 \nLoss on extinguishment of debt\n(15)\n(78)\n(186)\nOther expense\n(68)\n(60)\n(53)\nOther income\n102 \n410 \n75 \nLoss on disposal and sale of business interests\n(9)\n(1,683)\n(95)\nGoodwill impairment expense\n(777)\n \n \nAsset impairment expense\n(763)\n(1,575)\n(864)\nForeign currency transaction gains (losses)\n(77)\n(10)\n55 \nOther non-operating expense\n(175)\n \n(202)\nINCOME (LOSS) FROM CONTINUING OPERATIONS BEFORE TAXES AND EQUITY IN EARNINGS OF AFFILIATES\n(169)\n(1,064)\n488 \nIncome tax benefit (expense)\n(265)\n133 \n(216)\nNet equity in losses of affiliates\n(71)\n(24)\n(123)\nINCOME (LOSS) FROM CONTINUING OPERATIONS\n(505)\n(955)\n149 \nGain from disposal of discontinued businesses, net of income tax expense of $0, $1, and $0, respectively\n \n4 \n3 \nNET INCOME (LOSS)\n(505)\n(951)\n152 \nLess: Net loss (income) attributable to noncontrolling interests and redeemable stock of subsidiaries\n(41)\n542 \n(106)\nNET INCOME (LOSS) ATTRIBUTABLE TO THE AES CORPORATION\n$\n(546)\n$\n(409)\n$\n46 \nAMOUNTS ATTRIBUTABLE TO THE AES CORPORATION COMMON STOCKHOLDERS:\nIncome (loss) from continuing operations, net of tax\n$\n(546)\n$\n(413)\n$\n43 \nIncome from discontinued operations, net of tax\n \n4 \n3 \nNET INCOME (LOSS) ATTRIBUTABLE TO THE AES CORPORATION\n$\n(546)\n$\n(409)\n$\n46 \nBASIC EARNINGS PER SHARE:\nIncome (loss) from continuing operations attributable to The AES Corporation common stockholders, net of tax\n$\n(0.82)\n$\n(0.62)\n$\n0.06 \nIncome from discontinued operations attributable to The AES Corporation common stockholders, net of tax\n \n0.01 \n0.01 \nNET INCOME (LOSS) ATTRIBUTABLE TO THE AES CORPORATION COMMON STOCKHOLDERS\n$\n(0.82)\n$\n(0.61)\n$\n0.07 \nDILUTED EARNINGS PER SHARE:\nIncome (loss) from continuing operations attributable to The AES Corporation common stockholders, net of tax\n$\n(0.82)\n$\n(0.62)\n$\n0.06 \nIncome from discontinued operations attributable to The AES Corporation common stockholders, net of tax\n \n0.01 \n0.01 \nNET INCOME (LOSS) ATTRIBUTABLE TO THE AES CORPORATION COMMON STOCKHOLDERS\n$\n(0.82)\n$\n(0.61)\n$\n0.07 \nSee Accompanying Notes to Consolidated Financial Statements.\n"}]} +{"financebench_id": "financebench_id_10420", "company": "AES Corporation", "doc_name": "AES_2022_10K", "question_type": "metrics-generated", "question_reasoning": "Numerical reasoning", "domain_question_num": null, "question": "Based on the information provided primarily in the statement of financial position and the statement of income, what is AES's FY2022 return on assets (ROA)? ROA is defined as: FY2022 net income / (average total assets between FY2021 and FY2022). Round your answer to two decimal places.", "answer": "-0.02", "justification": "The metric in question was calculated using other simpler metrics. The various simpler metrics (from the current and, if relevant, previous fiscal year(s)) used were:\n\nMetric 1: Net income. This metric was located in the 10K as a single line item named: NET INCOME (LOSS) ATTRIBUTABLE TO THE AES CORPORATION.\n\nMetric 2: Total assets. This metric was located in the 10K as a single line item named: TOTAL ASSETS.", "dataset_subset_label": "OPEN_SOURCE", "evidence": [{"evidence_text": "128 \nConsolidated Balance Sheets\nDecember 31, 2022 and 2021\n2022\n2021\n(in millions, except share and per share data)\nASSETS\nCURRENT ASSETS\nCash and cash equivalents\n$\n1,374 \n$\n943 \nRestricted cash\n536 \n304 \nShort-term investments\n730 \n232 \nAccounts receivable, net of allowance for doubtful accounts of $5 and $5, respectively\n1,799 \n1,418 \nInventory\n1,055 \n604 \nPrepaid expenses\n98 \n142 \nOther current assets, net of CECL allowance of $2 and $0, respectively\n1,533 \n897 \nCurrent held-for-sale assets\n518 \n816 \nTotal current assets\n7,643 \n5,356 \nNONCURRENT ASSETS\nProperty, Plant and Equipment:\nLand\n470 \n426 \nElectric generation, distribution assets and other\n26,599 \n25,552 \nAccumulated depreciation\n(8,651)\n(8,486)\nConstruction in progress\n4,621 \n2,414 \nProperty, plant and equipment, net\n23,039 \n19,906 \nOther Assets:\nInvestments in and advances to affiliates\n952 \n1,080 \nDebt service reserves and other deposits\n177 \n237 \nGoodwill\n362 \n1,177 \nOther intangible assets, net of accumulated amortization of $434 and $385, respectively\n1,841 \n1,450 \nDeferred income taxes\n319 \n409 \nLoan receivable, net of allowance of $26\n1,051 \n \nOther noncurrent assets, net of allowance of $51 and $23, respectively\n2,979 \n2,188 \nNoncurrent held-for-sale assets\n \n1,160 \nTotal other assets\n7,681 \n7,701 \nTOTAL ASSETS\n$\n38,363 \n$\n32,963 \nLIABILITIES AND EQUITY\nCURRENT LIABILITIES\nAccounts payable\n$\n1,730 \n$\n1,153 \nAccrued interest\n249 \n182 \nAccrued non-income taxes\n249 \n266 \nAccrued and other liabilities\n2,151 \n1,205 \nNon-recourse debt, including $416 and $302, respectively, related to variable interest entities\n1,758 \n1,367 \nCurrent held-for-sale liabilities\n354 \n559 \nTotal current liabilities\n6,491 \n4,732 \nNONCURRENT LIABILITIES\nRecourse debt\n3,894 \n3,729 \nNon-recourse debt, including $2,295 and $2,223, respectively, related to variable interest entities\n17,846 \n13,603 \nDeferred income taxes\n1,139 \n977 \nOther noncurrent liabilities\n3,168 \n3,358 \nNoncurrent held-for-sale liabilities\n \n740 \nTotal noncurrent liabilities\n26,047 \n22,407 \nCommitments and Contingencies (see Notes 12 and 13)\nRedeemable stock of subsidiaries\n1,321 \n1,257 \nEQUITY\nTHE AES CORPORATION STOCKHOLDERS EQUITY\nPreferred stock (without par value, 50,000,000 shares authorized; 1,043,050 issued and outstanding at December 31, 2022 and\nDecember 31, 2021)\n838 \n838 \nCommon stock ($0.01 par value, 1,200,000,000 shares authorized; 818,790,001 issued and 668,743,464 outstanding at December\n31, 2022 and 818,717,043 issued and 666,793,625 outstanding at December 31, 2021)\n8 \n8 \nAdditional paid-in capital\n6,688 \n7,106 \nAccumulated deficit\n(1,635)\n(1,089)\nAccumulated other comprehensive loss\n(1,640)\n(2,220)\nTreasury stock, at cost (150,046,537 and 151,923,418 shares at December 31, 2022 and December 31, 2021, respectively)\n(1,822)\n(1,845)\nTotal AES Corporation stockholders equity\n2,437 \n2,798 \nNONCONTROLLING INTERESTS\n2,067 \n1,769 \nTotal equity\n4,504 \n4,567 \nTOTAL LIABILITIES AND EQUITY\n$\n38,363 \n$\n32,963 \nSee Accompanying Notes to Consolidated Financial Statements.", "doc_name": "AES_2022_10K", "evidence_page_num": 129, "evidence_text_full_page": "128 \nConsolidated Balance Sheets\nDecember 31, 2022 and 2021\n2022\n2021\n(in millions, except share and per share data)\nASSETS\nCURRENT ASSETS\nCash and cash equivalents\n$\n1,374 \n$\n943 \nRestricted cash\n536 \n304 \nShort-term investments\n730 \n232 \nAccounts receivable, net of allowance for doubtful accounts of $5 and $5, respectively\n1,799 \n1,418 \nInventory\n1,055 \n604 \nPrepaid expenses\n98 \n142 \nOther current assets, net of CECL allowance of $2 and $0, respectively\n1,533 \n897 \nCurrent held-for-sale assets\n518 \n816 \nTotal current assets\n7,643 \n5,356 \nNONCURRENT ASSETS\nProperty, Plant and Equipment:\nLand\n470 \n426 \nElectric generation, distribution assets and other\n26,599 \n25,552 \nAccumulated depreciation\n(8,651)\n(8,486)\nConstruction in progress\n4,621 \n2,414 \nProperty, plant and equipment, net\n23,039 \n19,906 \nOther Assets:\nInvestments in and advances to affiliates\n952 \n1,080 \nDebt service reserves and other deposits\n177 \n237 \nGoodwill\n362 \n1,177 \nOther intangible assets, net of accumulated amortization of $434 and $385, respectively\n1,841 \n1,450 \nDeferred income taxes\n319 \n409 \nLoan receivable, net of allowance of $26\n1,051 \n \nOther noncurrent assets, net of allowance of $51 and $23, respectively\n2,979 \n2,188 \nNoncurrent held-for-sale assets\n \n1,160 \nTotal other assets\n7,681 \n7,701 \nTOTAL ASSETS\n$\n38,363 \n$\n32,963 \nLIABILITIES AND EQUITY\nCURRENT LIABILITIES\nAccounts payable\n$\n1,730 \n$\n1,153 \nAccrued interest\n249 \n182 \nAccrued non-income taxes\n249 \n266 \nAccrued and other liabilities\n2,151 \n1,205 \nNon-recourse debt, including $416 and $302, respectively, related to variable interest entities\n1,758 \n1,367 \nCurrent held-for-sale liabilities\n354 \n559 \nTotal current liabilities\n6,491 \n4,732 \nNONCURRENT LIABILITIES\nRecourse debt\n3,894 \n3,729 \nNon-recourse debt, including $2,295 and $2,223, respectively, related to variable interest entities\n17,846 \n13,603 \nDeferred income taxes\n1,139 \n977 \nOther noncurrent liabilities\n3,168 \n3,358 \nNoncurrent held-for-sale liabilities\n \n740 \nTotal noncurrent liabilities\n26,047 \n22,407 \nCommitments and Contingencies (see Notes 12 and 13)\nRedeemable stock of subsidiaries\n1,321 \n1,257 \nEQUITY\nTHE AES CORPORATION STOCKHOLDERS EQUITY\nPreferred stock (without par value, 50,000,000 shares authorized; 1,043,050 issued and outstanding at December 31, 2022 and\nDecember 31, 2021)\n838 \n838 \nCommon stock ($0.01 par value, 1,200,000,000 shares authorized; 818,790,001 issued and 668,743,464 outstanding at December\n31, 2022 and 818,717,043 issued and 666,793,625 outstanding at December 31, 2021)\n8 \n8 \nAdditional paid-in capital\n6,688 \n7,106 \nAccumulated deficit\n(1,635)\n(1,089)\nAccumulated other comprehensive loss\n(1,640)\n(2,220)\nTreasury stock, at cost (150,046,537 and 151,923,418 shares at December 31, 2022 and December 31, 2021, respectively)\n(1,822)\n(1,845)\nTotal AES Corporation stockholders equity\n2,437 \n2,798 \nNONCONTROLLING INTERESTS\n2,067 \n1,769 \nTotal equity\n4,504 \n4,567 \nTOTAL LIABILITIES AND EQUITY\n$\n38,363 \n$\n32,963 \nSee Accompanying Notes to Consolidated Financial Statements.\n"}, {"evidence_text": "129 \nConsolidated Statements of Operations\nYears ended December 31, 2022, 2021, and 2020\n2022\n2021\n2020\n(in millions, except per share amounts)\nRevenue:\nRegulated\n$\n3,538 \n$\n2,868 \n$\n2,661 \nNon-Regulated\n9,079 \n8,273 \n6,999 \nTotal revenue\n12,617 \n11,141 \n9,660 \nCost of Sales:\nRegulated\n(3,162)\n(2,448)\n(2,235)\nNon-Regulated\n(6,907)\n(5,982)\n(4,732)\nTotal cost of sales\n(10,069)\n(8,430)\n(6,967)\nOperating margin\n2,548 \n2,711 \n2,693 \nGeneral and administrative expenses\n(207)\n(166)\n(165)\nInterest expense\n(1,117)\n(911)\n(1,038)\nInterest income\n389 \n298 \n268 \nLoss on extinguishment of debt\n(15)\n(78)\n(186)\nOther expense\n(68)\n(60)\n(53)\nOther income\n102 \n410 \n75 \nLoss on disposal and sale of business interests\n(9)\n(1,683)\n(95)\nGoodwill impairment expense\n(777)\n \n \nAsset impairment expense\n(763)\n(1,575)\n(864)\nForeign currency transaction gains (losses)\n(77)\n(10)\n55 \nOther non-operating expense\n(175)\n \n(202)\nINCOME (LOSS) FROM CONTINUING OPERATIONS BEFORE TAXES AND EQUITY IN EARNINGS OF AFFILIATES\n(169)\n(1,064)\n488 \nIncome tax benefit (expense)\n(265)\n133 \n(216)\nNet equity in losses of affiliates\n(71)\n(24)\n(123)\nINCOME (LOSS) FROM CONTINUING OPERATIONS\n(505)\n(955)\n149 \nGain from disposal of discontinued businesses, net of income tax expense of $0, $1, and $0, respectively\n \n4 \n3 \nNET INCOME (LOSS)\n(505)\n(951)\n152 \nLess: Net loss (income) attributable to noncontrolling interests and redeemable stock of subsidiaries\n(41)\n542 \n(106)\nNET INCOME (LOSS) ATTRIBUTABLE TO THE AES CORPORATION\n$\n(546)\n$\n(409)\n$\n46 \nAMOUNTS ATTRIBUTABLE TO THE AES CORPORATION COMMON STOCKHOLDERS:\nIncome (loss) from continuing operations, net of tax\n$\n(546)\n$\n(413)\n$\n43 \nIncome from discontinued operations, net of tax\n \n4 \n3 \nNET INCOME (LOSS) ATTRIBUTABLE TO THE AES CORPORATION\n$\n(546)\n$\n(409)\n$\n46 \nBASIC EARNINGS PER SHARE:\nIncome (loss) from continuing operations attributable to The AES Corporation common stockholders, net of tax\n$\n(0.82)\n$\n(0.62)\n$\n0.06 \nIncome from discontinued operations attributable to The AES Corporation common stockholders, net of tax\n \n0.01 \n0.01 \nNET INCOME (LOSS) ATTRIBUTABLE TO THE AES CORPORATION COMMON STOCKHOLDERS\n$\n(0.82)\n$\n(0.61)\n$\n0.07 \nDILUTED EARNINGS PER SHARE:\nIncome (loss) from continuing operations attributable to The AES Corporation common stockholders, net of tax\n$\n(0.82)\n$\n(0.62)\n$\n0.06 \nIncome from discontinued operations attributable to The AES Corporation common stockholders, net of tax\n \n0.01 \n0.01 \nNET INCOME (LOSS) ATTRIBUTABLE TO THE AES CORPORATION COMMON STOCKHOLDERS\n$\n(0.82)\n$\n(0.61)\n$\n0.07 \nSee Accompanying Notes to Consolidated Financial Statements.", "doc_name": "AES_2022_10K", "evidence_page_num": 131, "evidence_text_full_page": "129 \nConsolidated Statements of Operations\nYears ended December 31, 2022, 2021, and 2020\n2022\n2021\n2020\n(in millions, except per share amounts)\nRevenue:\nRegulated\n$\n3,538 \n$\n2,868 \n$\n2,661 \nNon-Regulated\n9,079 \n8,273 \n6,999 \nTotal revenue\n12,617 \n11,141 \n9,660 \nCost of Sales:\nRegulated\n(3,162)\n(2,448)\n(2,235)\nNon-Regulated\n(6,907)\n(5,982)\n(4,732)\nTotal cost of sales\n(10,069)\n(8,430)\n(6,967)\nOperating margin\n2,548 \n2,711 \n2,693 \nGeneral and administrative expenses\n(207)\n(166)\n(165)\nInterest expense\n(1,117)\n(911)\n(1,038)\nInterest income\n389 \n298 \n268 \nLoss on extinguishment of debt\n(15)\n(78)\n(186)\nOther expense\n(68)\n(60)\n(53)\nOther income\n102 \n410 \n75 \nLoss on disposal and sale of business interests\n(9)\n(1,683)\n(95)\nGoodwill impairment expense\n(777)\n \n \nAsset impairment expense\n(763)\n(1,575)\n(864)\nForeign currency transaction gains (losses)\n(77)\n(10)\n55 \nOther non-operating expense\n(175)\n \n(202)\nINCOME (LOSS) FROM CONTINUING OPERATIONS BEFORE TAXES AND EQUITY IN EARNINGS OF AFFILIATES\n(169)\n(1,064)\n488 \nIncome tax benefit (expense)\n(265)\n133 \n(216)\nNet equity in losses of affiliates\n(71)\n(24)\n(123)\nINCOME (LOSS) FROM CONTINUING OPERATIONS\n(505)\n(955)\n149 \nGain from disposal of discontinued businesses, net of income tax expense of $0, $1, and $0, respectively\n \n4 \n3 \nNET INCOME (LOSS)\n(505)\n(951)\n152 \nLess: Net loss (income) attributable to noncontrolling interests and redeemable stock of subsidiaries\n(41)\n542 \n(106)\nNET INCOME (LOSS) ATTRIBUTABLE TO THE AES CORPORATION\n$\n(546)\n$\n(409)\n$\n46 \nAMOUNTS ATTRIBUTABLE TO THE AES CORPORATION COMMON STOCKHOLDERS:\nIncome (loss) from continuing operations, net of tax\n$\n(546)\n$\n(413)\n$\n43 \nIncome from discontinued operations, net of tax\n \n4 \n3 \nNET INCOME (LOSS) ATTRIBUTABLE TO THE AES CORPORATION\n$\n(546)\n$\n(409)\n$\n46 \nBASIC EARNINGS PER SHARE:\nIncome (loss) from continuing operations attributable to The AES Corporation common stockholders, net of tax\n$\n(0.82)\n$\n(0.62)\n$\n0.06 \nIncome from discontinued operations attributable to The AES Corporation common stockholders, net of tax\n \n0.01 \n0.01 \nNET INCOME (LOSS) ATTRIBUTABLE TO THE AES CORPORATION COMMON STOCKHOLDERS\n$\n(0.82)\n$\n(0.61)\n$\n0.07 \nDILUTED EARNINGS PER SHARE:\nIncome (loss) from continuing operations attributable to The AES Corporation common stockholders, net of tax\n$\n(0.82)\n$\n(0.62)\n$\n0.06 \nIncome from discontinued operations attributable to The AES Corporation common stockholders, net of tax\n \n0.01 \n0.01 \nNET INCOME (LOSS) ATTRIBUTABLE TO THE AES CORPORATION COMMON STOCKHOLDERS\n$\n(0.82)\n$\n(0.61)\n$\n0.07 \nSee Accompanying Notes to Consolidated Financial Statements.\n"}]} +{"financebench_id": "financebench_id_00685", "company": "Best Buy", "doc_name": "BESTBUY_2023_10K", "question_type": "domain-relevant", "question_reasoning": "Logical reasoning (based on numerical reasoning) OR Logical reasoning", "domain_question_num": "dg03", "question": "Are Best Buy's gross margins historically consistent (not fluctuating more than roughly 2% each year)? If gross margins are not a relevant metric for a company like this, then please state that and explain why.", "answer": "Yes, the margins have been consistent, there has been a minor decline of 1.1% in gross margins between FY2022 and FY2023.", "justification": "Gross Profit/Revenue\n9912/46298\n11640/51761", "dataset_subset_label": "OPEN_SOURCE", "evidence": [{"evidence_text": "Consolidated Statements of Earnings\n$ and shares in millions, except per share amounts\n \n \n \n \n \n \n \n \n \n \n \n \n \nFiscal Years Ended\nJanuary 28, 2023\n \nJanuary 29, 2022\n \nJanuary 30, 2021\nRevenue\n$\n 46,298 \n \n$\n 51,761 \n \n$\n 47,262 \nCost of sales\n \n 36,386 \n \n \n 40,121 \n \n \n 36,689 \nGross profit\n \n 9,912 \n \n \n 11,640 \n \n \n 10,573 \nSelling, general and administrative expenses\n \n 7,970 \n \n \n 8,635 \n \n \n 7,928 \nRestructuring charges\n \n 147 \n \n \n (34) \n \n \n 254 \nOperating income\n \n 1,795 \n \n \n 3,039 \n \n \n 2,391 \nOther income (expense):\n \n \n \n \n \n \n \n \n \n \n \nInvestment income and other\n \n 28 \n \n \n 10 \n \n \n 38 \nInterest expense\n \n (35) \n \n \n (25) \n \n \n (52) \nEarnings before income tax expense and equity in income of affiliates\n \n 1,788 \n \n \n 3,024 \n \n \n 2,377 \nIncome tax expense\n \n 370 \n \n \n 574 \n \n \n 579 \nEquity in income of affiliates\n \n 1 \n \n \n 4 \n \n \n - \nNet earnings\n$\n 1,419 \n \n$\n 2,454 \n \n$\n 1,798", "doc_name": "BESTBUY_2023_10K", "evidence_page_num": 39, "evidence_text_full_page": " \nConsolidated Statements of Earnings\n$ and shares in millions, except per share amounts\n \n \n \n \n \n \n \n \n \n \n \n \n \nFiscal Years Ended\nJanuary 28, 2023\n \nJanuary 29, 2022\n \nJanuary 30, 2021\nRevenue\n$\n 46,298 \n \n$\n 51,761 \n \n$\n 47,262 \nCost of sales\n \n 36,386 \n \n \n 40,121 \n \n \n 36,689 \nGross profit\n \n 9,912 \n \n \n 11,640 \n \n \n 10,573 \nSelling, general and administrative expenses\n \n 7,970 \n \n \n 8,635 \n \n \n 7,928 \nRestructuring charges\n \n 147 \n \n \n (34) \n \n \n 254 \nOperating income\n \n 1,795 \n \n \n 3,039 \n \n \n 2,391 \nOther income (expense):\n \n \n \n \n \n \n \n \n \n \n \nInvestment income and other\n \n 28 \n \n \n 10 \n \n \n 38 \nInterest expense\n \n (35) \n \n \n (25) \n \n \n (52) \nEarnings before income tax expense and equity in income of affiliates\n \n 1,788 \n \n \n 3,024 \n \n \n 2,377 \nIncome tax expense\n \n 370 \n \n \n 574 \n \n \n 579 \nEquity in income of affiliates\n \n 1 \n \n \n 4 \n \n \n - \nNet earnings\n$\n 1,419 \n \n$\n 2,454 \n \n$\n 1,798 \n \n \n \n \n \n \n \n \n \n \n \n \nBasic earnings per share\n$\n 6.31 \n \n$\n 9.94 \n \n$\n 6.93 \nDiluted earnings per share\n$\n 6.29 \n \n$\n 9.84 \n \n$\n 6.84 \n \n \n \n \n \n \n \n \n \n \n \n \nWeighted-average common shares outstanding:\n \n \n \n \n \n \n \n \n \n \n \nBasic\n \n 224.8 \n \n \n 246.8 \n \n \n 259.6 \nDiluted\n \n 225.7 \n \n \n 249.3 \n \n \n 263.0 \n \nSee Notes to Consolidated Financial Statements.\n \n \n40\n"}]} +{"financebench_id": "financebench_id_01077", "company": "Best Buy", "doc_name": "BESTBUY_2023_10K", "question_type": "domain-relevant", "question_reasoning": "Information extraction", "domain_question_num": "dg10", "question": "What are major acquisitions that Best Buy has done in FY2023, FY2022 and FY2021?", "answer": "Best Buy closed two acquisitions, both these companies were already partially owned by Best Buy, but Best Buy acquired all outstanding shares of these two companies during FY 2022: (1) Current Health Ltd and (2) Two Peaks, LLC d/b/a Yardbird Furniture", "justification": null, "dataset_subset_label": "OPEN_SOURCE", "evidence": [{"evidence_text": "Acquisitions\n \nCurrent Health Ltd.\n \nIn fiscal 2022, we acquired all of the outstanding shares of Current Health Ltd. (Current Health), a care-at-home technology platform, on November 2, 2021, for \nnet cash consideration of $389 million. The acquired assets included $351 million of goodwill that was assigned to our Best Buy Health reporting unit and was \ndeductible for income tax purposes. The acquisition is aligned with our focus in virtual care to enable people in their homes to connect seamlessly with their \nhealth care providers and is included in our Domestic reportable segment and Services revenue category. The acquisition was accounted for using the \nacquisition method of accounting for business combinations and was not material to the results of operations.\n \nTwo Peaks, LLC d/b/a Yardbird Furniture\n \nIn fiscal 2022, we acquired all of the outstanding shares of Two Peaks, LLC d/b/a Yardbird Furniture (Yardbird), a direct-to-consumer outdoor furniture company, \non November 4, 2021, for net cash consideration of $79 million. The acquired assets included $47 million of goodwill that was assigned to our Best Buy Domestic \nreporting unit and was deductible for income tax purposes. The acquisition expands our assortment in categories like outdoor living, as more and more \nconsumers look to make over or upgrade their outdoor living spaces. The acquisition was accounted for using the acquisition method of accounting for business \ncombinations and was not material to the results of our operations.", "doc_name": "BESTBUY_2023_10K", "evidence_page_num": 50, "evidence_text_full_page": "Vendor Allowances\n \nWe receive funds from our merchandise vendors through a variety of programs and arrangements, primarily in the form of purchases-based or sales-based \nvolumes and for product advertising and placement. We recognize allowances based on purchases and sales as a reduction of cost of sales when the associated \ninventory is sold. Allowances for advertising and placement are recognized as a reduction of cost of sales ratably over the corresponding performance period. \nFunds that are determined to be a reimbursement of specific, incremental and identifiable costs incurred to sell a vendors products are recorded as an offset to \nthe related expense within SG&A when incurred.\n \nAdvertising Costs\n \nAdvertising costs, which are included in SG&A, are expensed the first time the advertisement runs. Advertising costs consist primarily of digital advertisements. \nAdvertising expenses were $864 million, $915 million and $819 million in fiscal 2023, fiscal 2022 and fiscal 2021, respectively.\n \nStock-Based Compensation\n \nWe recognize stock-based compensation expense for the fair value of our stock-based compensation awards, which is determined based on the closing market \nprice of our stock at the date of grant for time-based and performance-based share awards, and Monte-Carlo simulation for market-based share awards. \nCompensation expense is recognized on a straight-line basis over the period in which services are required, except for performance-based share awards that \nvest on a graded basis, in which case the expense is front-loaded or recognized on a graded-attribution basis. Forfeitures are expensed as incurred or upon \ntermination.\n \nComprehensive Income (Loss)\n \nComprehensive income (loss) is computed as net earnings plus certain other items that are recorded directly to shareholders equity.\n \n2. Acquisitions\n \nCurrent Health Ltd.\n \nIn fiscal 2022, we acquired all of the outstanding shares of Current Health Ltd. (Current Health), a care-at-home technology platform, on November 2, 2021, for \nnet cash consideration of $389 million. The acquired assets included $351 million of goodwill that was assigned to our Best Buy Health reporting unit and was \ndeductible for income tax purposes. The acquisition is aligned with our focus in virtual care to enable people in their homes to connect seamlessly with their \nhealth care providers and is included in our Domestic reportable segment and Services revenue category. The acquisition was accounted for using the \nacquisition method of accounting for business combinations and was not material to the results of operations.\n \nTwo Peaks, LLC d/b/a Yardbird Furniture\n \nIn fiscal 2022, we acquired all of the outstanding shares of Two Peaks, LLC d/b/a Yardbird Furniture (Yardbird), a direct-to-consumer outdoor furniture company, \non November 4, 2021, for net cash consideration of $79 million. The acquired assets included $47 million of goodwill that was assigned to our Best Buy Domestic \nreporting unit and was deductible for income tax purposes. The acquisition expands our assortment in categories like outdoor living, as more and more \nconsumers look to make over or upgrade their outdoor living spaces. The acquisition was accounted for using the acquisition method of accounting for business \ncombinations and was not material to the results of our operations.\n \n3. Restructuring\n \n \nRestructuring charges were as follows ($ in millions):\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n2023\n \n2022\n \n2021\nFiscal 2023 Resource Optimization Initiative\n \n \n$\n 145 \n \n$\n - \n \n$\n - \nMexico Exit and Strategic Realignment(1)\n \n \n \n 2 \n \n \n (41) \n \n \n 277 \nFiscal 2020 U.S. Retail Operating Model Changes\n \n -\n \n \n 1 \n \n \n -\n \nTotal\n \n \n \n \n \n$\n 147 \n \n$\n (40) \n \n$\n 277 \n \n(1)\nIncludes ($6) million and $23 million related to inventory markdowns recorded in Cost of sales on our Consolidated Statements of Earnings in fiscal 2022 and fiscal 2021, respectively.\n \nFiscal 2023 Resource Optimization Initiative\n \nIn light of ongoing changes in business trends, during the second quarter of fiscal 2023, we commenced an enterprise-wide initiative to better align our spending \nwith critical strategies and operations, as well as to optimize our cost structure. Charges incurred relate to employee termination benefits within our Domestic and \nInternational segments of $140 million and $5 million, respectively. We currently do not expect the remaining charges in fiscal 2024 related to this initiative to be \nmaterial to the results of our operations.\n \nAll charges incurred related to this initiative were from continuing operations and were presented within Restructuring charges on our Consolidated Statements of \nEarnings. \n \n51\n"}]} +{"financebench_id": "financebench_id_01275", "company": "Best Buy", "doc_name": "BESTBUY_2023_10K", "question_type": "domain-relevant", "question_reasoning": "Numerical reasoning", "domain_question_num": "dg19", "question": "Among operations, investing, and financing activities, which brought in the most (or lost the least) cash flow for Best Buy in FY2023?", "answer": "Best Buy generated the most cash flow from operating activities in FY 2023 ($1.8 bn)", "justification": null, "dataset_subset_label": "OPEN_SOURCE", "evidence": [{"evidence_text": "Consolidated Statements of Cash Flows\n$ in millions\n \n \n \n \n \n \n \n \n \n \n \n \n \n \nFiscal Years Ended\nJanuary 28, 2023\n \nJanuary 29, 2022\n \nJanuary 30, 2021\nOperating activities\n \n \n \n \n \n \n \n \n \n \n \nNet earnings\n$\n 1,419 \n \n$\n 2,454 \n \n$\n 1,798 \n \nAdjustments to reconcile net earnings to total cash provided by operating activities:\n \n \n \n \n \n \n \n \nDepreciation and amortization\n \n 918 \n \n \n 869 \n \n \n 839 \nRestructuring charges\n \n 147 \n \n \n (34) \n \n \n 254 \nStock-based compensation\n \n 138 \n \n \n 141 \n \n \n 135 \nDeferred income taxes\n \n 51 \n \n \n 14 \n \n \n (36) \nOther, net\n \n 12 \n \n \n 11 \n \n \n 3 \nChanges in operating assets and liabilities, net of acquired assets and liabilities:\n \n \n \n \n \n \n \n \n \nReceivables\n \n (103) \n \n \n 17 \n \n \n 73 \nMerchandise inventories\n \n 809 \n \n \n (328) \n \n \n (435) \nOther assets\n \n (21) \n \n \n (14) \n \n \n (51) \nAccounts payable\n \n (1,099) \n \n \n (201) \n \n \n 1,676 \nIncome taxes\n \n 36 \n \n \n (156) \n \n \n 173 \nOther liabilities\n \n (483) \n \n \n 479 \n \n \n 498 \nTotal cash provided by operating activities\n \n 1,824 \n \n \n 3,252 \n \n \n 4,927 \nInvesting activities\n \n \n \n \n \n \n \n \n \n \n \nAdditions to property and equipment, net of $35, $46 and $32, respectively, of non-cash \ncapital expenditures\n \n (930) \n \n \n (737) \n \n \n (713) \nPurchases of investments\n \n (46) \n \n \n (233) \n \n \n (620) \nSales of investments\n \n 7 \n \n \n 66 \n \n \n 546 \nAcquisitions, net of cash acquired\n \n - \n \n \n (468) \n \n \n - \nOther, net\n \n 7 \n \n \n - \n \n \n (1) \nTotal cash used in investing activities\n \n (962) \n \n \n (1,372) \n \n \n (788) \nFinancing activities\n \n \n \n \n \n \n \n \n \n \n \nRepurchase of common stock\n \n (1,014) \n \n \n (3,502) \n \n \n (312) \nIssuance of common stock\n \n 16 \n \n \n 29 \n \n \n 28 \nDividends paid\n \n (789) \n \n \n (688) \n \n \n (568) \nBorrowings of debt\n \n - \n \n \n - \n \n \n 1,892 \nRepayments of debt\n \n (19) \n \n \n (133) \n \n \n (1,916) \nOther, net\n \n - \n \n \n (3) \n \n \n - \nTotal cash used in financing activities\n \n (1,806) \n \n \n (4,297) \n \n \n (876) \n \n \n \n \n \n \n \n \n \n \n \n \nEffect of exchange rate changes on cash\n \n (8) \n \n \n (3) \n \n \n 7 \nIncrease (decrease) in cash, cash equivalents and restricted cash\n \n (952) \n \n \n (2,420) \n \n \n 3,270 \nCash, cash equivalents and restricted cash at beginning of period\n \n 3,205 \n \n \n 5,625 \n \n \n 2,355 \nCash, cash equivalents and restricted cash at end of period\n$\n 2,253 \n \n$\n 3,205 \n \n$\n 5,625", "doc_name": "BESTBUY_2023_10K", "evidence_page_num": 41, "evidence_text_full_page": " \nConsolidated Statements of Cash Flows\n$ in millions\n \n \n \n \n \n \n \n \n \n \n \n \n \n \nFiscal Years Ended\nJanuary 28, 2023\n \nJanuary 29, 2022\n \nJanuary 30, 2021\nOperating activities\n \n \n \n \n \n \n \n \n \n \n \nNet earnings\n$\n 1,419 \n \n$\n 2,454 \n \n$\n 1,798 \n \nAdjustments to reconcile net earnings to total cash provided by operating activities:\n \n \n \n \n \n \n \n \nDepreciation and amortization\n \n 918 \n \n \n 869 \n \n \n 839 \nRestructuring charges\n \n 147 \n \n \n (34) \n \n \n 254 \nStock-based compensation\n \n 138 \n \n \n 141 \n \n \n 135 \nDeferred income taxes\n \n 51 \n \n \n 14 \n \n \n (36) \nOther, net\n \n 12 \n \n \n 11 \n \n \n 3 \nChanges in operating assets and liabilities, net of acquired assets and liabilities:\n \n \n \n \n \n \n \n \n \nReceivables\n \n (103) \n \n \n 17 \n \n \n 73 \nMerchandise inventories\n \n 809 \n \n \n (328) \n \n \n (435) \nOther assets\n \n (21) \n \n \n (14) \n \n \n (51) \nAccounts payable\n \n (1,099) \n \n \n (201) \n \n \n 1,676 \nIncome taxes\n \n 36 \n \n \n (156) \n \n \n 173 \nOther liabilities\n \n (483) \n \n \n 479 \n \n \n 498 \nTotal cash provided by operating activities\n \n 1,824 \n \n \n 3,252 \n \n \n 4,927 \nInvesting activities\n \n \n \n \n \n \n \n \n \n \n \nAdditions to property and equipment, net of $35, $46 and $32, respectively, of non-cash \ncapital expenditures\n \n (930) \n \n \n (737) \n \n \n (713) \nPurchases of investments\n \n (46) \n \n \n (233) \n \n \n (620) \nSales of investments\n \n 7 \n \n \n 66 \n \n \n 546 \nAcquisitions, net of cash acquired\n \n - \n \n \n (468) \n \n \n - \nOther, net\n \n 7 \n \n \n - \n \n \n (1) \nTotal cash used in investing activities\n \n (962) \n \n \n (1,372) \n \n \n (788) \nFinancing activities\n \n \n \n \n \n \n \n \n \n \n \nRepurchase of common stock\n \n (1,014) \n \n \n (3,502) \n \n \n (312) \nIssuance of common stock\n \n 16 \n \n \n 29 \n \n \n 28 \nDividends paid\n \n (789) \n \n \n (688) \n \n \n (568) \nBorrowings of debt\n \n - \n \n \n - \n \n \n 1,892 \nRepayments of debt\n \n (19) \n \n \n (133) \n \n \n (1,916) \nOther, net\n \n - \n \n \n (3) \n \n \n - \nTotal cash used in financing activities\n \n (1,806) \n \n \n (4,297) \n \n \n (876) \n \n \n \n \n \n \n \n \n \n \n \n \nEffect of exchange rate changes on cash\n \n (8) \n \n \n (3) \n \n \n 7 \nIncrease (decrease) in cash, cash equivalents and restricted cash\n \n (952) \n \n \n (2,420) \n \n \n 3,270 \nCash, cash equivalents and restricted cash at beginning of period\n \n 3,205 \n \n \n 5,625 \n \n \n 2,355 \nCash, cash equivalents and restricted cash at end of period\n$\n 2,253 \n \n$\n 3,205 \n \n$\n 5,625 \n \n \n \n \n \n \n \n \n \n \n \n \n \nSupplemental cash flow information\n \n \n \n \n \n \n \n \n \n \n \nIncome taxes paid\n$\n 283 \n \n$\n 716 \n \n$\n 442 \nInterest paid\n$\n 31 \n \n$\n 22 \n \n$\n 50 \n \nSee Notes to Consolidated Financial Statements.\n \n \n42\n"}]} +{"financebench_id": "financebench_id_01488", "company": "Johnson & Johnson", "doc_name": "JOHNSON_JOHNSON_2023_8K_dated-2023-08-30", "question_type": "novel-generated", "question_reasoning": null, "domain_question_num": null, "question": "Which business segment of JnJ will be treated as a discontinued operation from August 30, 2023 onward?", "answer": "The Consumer Health business segment will be treated as a discontinued operation from August 30, 2023 onward.", "justification": null, "dataset_subset_label": "OPEN_SOURCE", "evidence": [{"evidence_text": "Exhibit 99.1\nJohnson & Johnson Announces Updated Financials and 2023 Guidance Following Completion of the Kenvue\nSeparation\n\nCompany expects increased 2023 Reported Sales Growth of 7.0% - 8.0%, Operational Sales Growth of 7.5% - 8.5%, and\nAdjusted Operational Sales Growth of 6.2% - 7.2%; Figures exclude the COVID-19 Vaccine\n\nCompany expects 2023 Adjusted Reported Earnings Per Share (EPS) of $10.00 - $10.10, reflecting increased growth of\n12.5% at the mid-point and Adjusted Operational EPS of $9.90 - $10.00, reflecting increased growth of 11.5% at the mid-\npoint\n\nCompany reduced outstanding share count by approximately 191 million; 2023 guidance reflects only a partial-year benefit\nof approximately 73.5 million shares or $0.28 benefit to EPS\n\nCompany secured $13.2 billion in cash proceeds from the Kenvue debt offering and initial public offering and maintains 9.5%\nof equity stake in Kenvue\n\nCompany maintains its quarterly dividend of $1.19 per share\nNew Brunswick, N.J. (August 30, 2023) Johnson & Johnson (NYSE: JNJ) (the Company) today announced updates to its financials and\n2023 guidance which reflect its operations as a company focused on transformational innovation in Pharmaceutical and MedTech. The\nCompany has published a recorded webinar for investors to provide additional context behind the updated financials and 2023 guidance\nfound in this release, which may be accessed by visiting the Investors section of the Company's website at webcasts & presentations.\nThe completion of this transaction uniquely positions Johnson & Johnson as a Pharmaceutical and MedTech company focused on delivering\ntransformative healthcare solutions to patients, said Joaquin Duato, Chairman of the Board and Chief Executive Officer. We are incredibly\nproud of the focus and dedication of our employees worldwide to achieve this milestone, which we are confident will unlock near- and long-\nterm value for all of our stakeholders.\nAs previously announced, the Company recently completed an exchange offer to finalize the separation of Kenvue Inc., formerly Johnson &\nJohnsons Consumer Health business. As a result of the completion of the exchange offer, Johnson & Johnson will now present its\nConsumer Health business financial results as discontinued operations, including a gain of approximately $20 billion in the third quarter of\n2023", "doc_name": "JOHNSON_JOHNSON_2023_8K_dated-2023-08-30", "evidence_page_num": 3, "evidence_text_full_page": "Exhibit 99.1\nJohnson & Johnson Announces Updated Financials and 2023 Guidance Following Completion of the Kenvue\nSeparation\n\nCompany expects increased 2023 Reported Sales Growth of 7.0% - 8.0%, Operational Sales Growth of 7.5% - 8.5%, and\nAdjusted Operational Sales Growth of 6.2% - 7.2%; Figures exclude the COVID-19 Vaccine\n\nCompany expects 2023 Adjusted Reported Earnings Per Share (EPS) of $10.00 - $10.10, reflecting increased growth of\n12.5% at the mid-point and Adjusted Operational EPS of $9.90 - $10.00, reflecting increased growth of 11.5% at the mid-\npoint\n\nCompany reduced outstanding share count by approximately 191 million; 2023 guidance reflects only a partial-year benefit\nof approximately 73.5 million shares or $0.28 benefit to EPS\n\nCompany secured $13.2 billion in cash proceeds from the Kenvue debt offering and initial public offering and maintains 9.5%\nof equity stake in Kenvue\n\nCompany maintains its quarterly dividend of $1.19 per share\nNew Brunswick, N.J. (August 30, 2023) Johnson & Johnson (NYSE: JNJ) (the Company) today announced updates to its financials and\n2023 guidance which reflect its operations as a company focused on transformational innovation in Pharmaceutical and MedTech. The\nCompany has published a recorded webinar for investors to provide additional context behind the updated financials and 2023 guidance\nfound in this release, which may be accessed by visiting the Investors section of the Company's website at webcasts & presentations.\nThe completion of this transaction uniquely positions Johnson & Johnson as a Pharmaceutical and MedTech company focused on delivering\ntransformative healthcare solutions to patients, said Joaquin Duato, Chairman of the Board and Chief Executive Officer. We are incredibly\nproud of the focus and dedication of our employees worldwide to achieve this milestone, which we are confident will unlock near- and long-\nterm value for all of our stakeholders.\nAs previously announced, the Company recently completed an exchange offer to finalize the separation of Kenvue Inc., formerly Johnson &\nJohnsons Consumer Health business. As a result of the completion of the exchange offer, Johnson & Johnson will now present its\nConsumer Health business financial results as discontinued operations, including a gain of approximately $20 billion in the third quarter of\n2023.\n"}]} +{"financebench_id": "financebench_id_01490", "company": "Johnson & Johnson", "doc_name": "JOHNSON_JOHNSON_2023_8K_dated-2023-08-30", "question_type": "novel-generated", "question_reasoning": null, "domain_question_num": null, "question": "What is the amount of the gain accruing to JnJ as a result of the separation of its Consumer Health business segment, as of August 30, 2023?", "answer": "JnJ will make a gain of approximately $20 billion from the separation of its Consumer Health business segment.", "justification": null, "dataset_subset_label": "OPEN_SOURCE", "evidence": [{"evidence_text": "Exhibit 99.1\nJohnson & Johnson Announces Updated Financials and 2023 Guidance Following Completion of the Kenvue\nSeparation\n\nCompany expects increased 2023 Reported Sales Growth of 7.0% - 8.0%, Operational Sales Growth of 7.5% - 8.5%, and\nAdjusted Operational Sales Growth of 6.2% - 7.2%; Figures exclude the COVID-19 Vaccine\n\nCompany expects 2023 Adjusted Reported Earnings Per Share (EPS) of $10.00 - $10.10, reflecting increased growth of\n12.5% at the mid-point and Adjusted Operational EPS of $9.90 - $10.00, reflecting increased growth of 11.5% at the mid-\npoint\n\nCompany reduced outstanding share count by approximately 191 million; 2023 guidance reflects only a partial-year benefit\nof approximately 73.5 million shares or $0.28 benefit to EPS\n\nCompany secured $13.2 billion in cash proceeds from the Kenvue debt offering and initial public offering and maintains 9.5%\nof equity stake in Kenvue\n\nCompany maintains its quarterly dividend of $1.19 per share\nNew Brunswick, N.J. (August 30, 2023) Johnson & Johnson (NYSE: JNJ) (the Company) today announced updates to its financials and\n2023 guidance which reflect its operations as a company focused on transformational innovation in Pharmaceutical and MedTech. The\nCompany has published a recorded webinar for investors to provide additional context behind the updated financials and 2023 guidance\nfound in this release, which may be accessed by visiting the Investors section of the Company's website at webcasts & presentations.\nThe completion of this transaction uniquely positions Johnson & Johnson as a Pharmaceutical and MedTech company focused on delivering\ntransformative healthcare solutions to patients, said Joaquin Duato, Chairman of the Board and Chief Executive Officer. We are incredibly\nproud of the focus and dedication of our employees worldwide to achieve this milestone, which we are confident will unlock near- and long-\nterm value for all of our stakeholders.\nAs previously announced, the Company recently completed an exchange offer to finalize the separation of Kenvue Inc., formerly Johnson &\nJohnsons Consumer Health business. As a result of the completion of the exchange offer, Johnson & Johnson will now present its\nConsumer Health business financial results as discontinued operations, including a gain of approximately $20 billion in the third quarter of\n2023.", "doc_name": "JOHNSON_JOHNSON_2023_8K_dated-2023-08-30", "evidence_page_num": 3, "evidence_text_full_page": "Exhibit 99.1\nJohnson & Johnson Announces Updated Financials and 2023 Guidance Following Completion of the Kenvue\nSeparation\n\nCompany expects increased 2023 Reported Sales Growth of 7.0% - 8.0%, Operational Sales Growth of 7.5% - 8.5%, and\nAdjusted Operational Sales Growth of 6.2% - 7.2%; Figures exclude the COVID-19 Vaccine\n\nCompany expects 2023 Adjusted Reported Earnings Per Share (EPS) of $10.00 - $10.10, reflecting increased growth of\n12.5% at the mid-point and Adjusted Operational EPS of $9.90 - $10.00, reflecting increased growth of 11.5% at the mid-\npoint\n\nCompany reduced outstanding share count by approximately 191 million; 2023 guidance reflects only a partial-year benefit\nof approximately 73.5 million shares or $0.28 benefit to EPS\n\nCompany secured $13.2 billion in cash proceeds from the Kenvue debt offering and initial public offering and maintains 9.5%\nof equity stake in Kenvue\n\nCompany maintains its quarterly dividend of $1.19 per share\nNew Brunswick, N.J. (August 30, 2023) Johnson & Johnson (NYSE: JNJ) (the Company) today announced updates to its financials and\n2023 guidance which reflect its operations as a company focused on transformational innovation in Pharmaceutical and MedTech. The\nCompany has published a recorded webinar for investors to provide additional context behind the updated financials and 2023 guidance\nfound in this release, which may be accessed by visiting the Investors section of the Company's website at webcasts & presentations.\nThe completion of this transaction uniquely positions Johnson & Johnson as a Pharmaceutical and MedTech company focused on delivering\ntransformative healthcare solutions to patients, said Joaquin Duato, Chairman of the Board and Chief Executive Officer. We are incredibly\nproud of the focus and dedication of our employees worldwide to achieve this milestone, which we are confident will unlock near- and long-\nterm value for all of our stakeholders.\nAs previously announced, the Company recently completed an exchange offer to finalize the separation of Kenvue Inc., formerly Johnson &\nJohnsons Consumer Health business. As a result of the completion of the exchange offer, Johnson & Johnson will now present its\nConsumer Health business financial results as discontinued operations, including a gain of approximately $20 billion in the third quarter of\n2023.\n"}]} +{"financebench_id": "financebench_id_01491", "company": "Johnson & Johnson", "doc_name": "JOHNSON_JOHNSON_2023_8K_dated-2023-08-30", "question_type": "novel-generated", "question_reasoning": null, "domain_question_num": null, "question": "What is the amount of the cash proceeds that JnJ realised from the separation of Kenvue (formerly Consumer Health business segment), as of August 30, 2023?", "answer": "JnJ realised $13.2 billion in cash proceeds from the separation of Kenvue.", "justification": null, "dataset_subset_label": "OPEN_SOURCE", "evidence": [{"evidence_text": "Exhibit 99.1\nJohnson & Johnson Announces Updated Financials and 2023 Guidance Following Completion of the Kenvue\nSeparation\n\nCompany expects increased 2023 Reported Sales Growth of 7.0% - 8.0%, Operational Sales Growth of 7.5% - 8.5%, and\nAdjusted Operational Sales Growth of 6.2% - 7.2%; Figures exclude the COVID-19 Vaccine\n\nCompany expects 2023 Adjusted Reported Earnings Per Share (EPS) of $10.00 - $10.10, reflecting increased growth of\n12.5% at the mid-point and Adjusted Operational EPS of $9.90 - $10.00, reflecting increased growth of 11.5% at the mid-\npoint\n\nCompany reduced outstanding share count by approximately 191 million; 2023 guidance reflects only a partial-year benefit\nof approximately 73.5 million shares or $0.28 benefit to EPS\n\nCompany secured $13.2 billion in cash proceeds from the Kenvue debt offering and initial public offering and maintains 9.5%\nof equity stake in Kenvue\n\nCompany maintains its quarterly dividend of $1.19 per share\nNew Brunswick, N.J. (August 30, 2023) Johnson & Johnson (NYSE: JNJ) (the Company) today announced updates to its financials and\n2023 guidance which reflect its operations as a company focused on transformational innovation in Pharmaceutical and MedTech. The\nCompany has published a recorded webinar for investors to provide additional context behind the updated financials and 2023 guidance\nfound in this release, which may be accessed by visiting the Investors section of the Company's website at webcasts & presentations.\nThe completion of this transaction uniquely positions Johnson & Johnson as a Pharmaceutical and MedTech company focused on delivering\ntransformative healthcare solutions to patients, said Joaquin Duato, Chairman of the Board and Chief Executive Officer. We are incredibly\nproud of the focus and dedication of our employees worldwide to achieve this milestone, which we are confident will unlock near- and long-\nterm value for all of our stakeholders.\nAs previously announced, the Company recently completed an exchange offer to finalize the separation of Kenvue Inc., formerly Johnson &\nJohnsons Consumer Health business. As a result of the completion of the exchange offer, Johnson & Johnson will now present its\nConsumer Health business financial results as discontinued operations, including a gain of approximately $20 billion in the third quarter of\n2023.", "doc_name": "JOHNSON_JOHNSON_2023_8K_dated-2023-08-30", "evidence_page_num": 3, "evidence_text_full_page": "Exhibit 99.1\nJohnson & Johnson Announces Updated Financials and 2023 Guidance Following Completion of the Kenvue\nSeparation\n\nCompany expects increased 2023 Reported Sales Growth of 7.0% - 8.0%, Operational Sales Growth of 7.5% - 8.5%, and\nAdjusted Operational Sales Growth of 6.2% - 7.2%; Figures exclude the COVID-19 Vaccine\n\nCompany expects 2023 Adjusted Reported Earnings Per Share (EPS) of $10.00 - $10.10, reflecting increased growth of\n12.5% at the mid-point and Adjusted Operational EPS of $9.90 - $10.00, reflecting increased growth of 11.5% at the mid-\npoint\n\nCompany reduced outstanding share count by approximately 191 million; 2023 guidance reflects only a partial-year benefit\nof approximately 73.5 million shares or $0.28 benefit to EPS\n\nCompany secured $13.2 billion in cash proceeds from the Kenvue debt offering and initial public offering and maintains 9.5%\nof equity stake in Kenvue\n\nCompany maintains its quarterly dividend of $1.19 per share\nNew Brunswick, N.J. (August 30, 2023) Johnson & Johnson (NYSE: JNJ) (the Company) today announced updates to its financials and\n2023 guidance which reflect its operations as a company focused on transformational innovation in Pharmaceutical and MedTech. The\nCompany has published a recorded webinar for investors to provide additional context behind the updated financials and 2023 guidance\nfound in this release, which may be accessed by visiting the Investors section of the Company's website at webcasts & presentations.\nThe completion of this transaction uniquely positions Johnson & Johnson as a Pharmaceutical and MedTech company focused on delivering\ntransformative healthcare solutions to patients, said Joaquin Duato, Chairman of the Board and Chief Executive Officer. We are incredibly\nproud of the focus and dedication of our employees worldwide to achieve this milestone, which we are confident will unlock near- and long-\nterm value for all of our stakeholders.\nAs previously announced, the Company recently completed an exchange offer to finalize the separation of Kenvue Inc., formerly Johnson &\nJohnsons Consumer Health business. As a result of the completion of the exchange offer, Johnson & Johnson will now present its\nConsumer Health business financial results as discontinued operations, including a gain of approximately $20 billion in the third quarter of\n2023.\n"}]} +{"financebench_id": "financebench_id_00601", "company": "Ulta Beauty", "doc_name": "ULTABEAUTY_2023Q4_EARNINGS", "question_type": "novel-generated", "question_reasoning": null, "domain_question_num": null, "question": "What drove the reduction in SG&A expense as a percent of net sales in FY2023?", "answer": "Lower marketing expenses and leverage of incentive compensation due to higher sales. The answer here assumes FY2023 refers to the 12 months ended on January 28, 2023 (although the company refers to this period as its fiscal 2022.", "justification": "Fiscal 2022 = FY2023. Fiscal 2021 = FY2022.", "dataset_subset_label": "OPEN_SOURCE", "evidence": [{"evidence_text": "For the Full Year of Fiscal 2022\nNet sales increased 18.3% to $10.2 billion compared to $8.6 billion in fiscal 2021,\nprimarily due to the favorable impact from the continued resilience of the beauty\ncategory, retail price increases, the impact of new brands and product innovation,\nincreased social occasions, and fewer COVID-19 limitations compared to fiscal 2021.\nComparable sales increased 15.6% compared to an increase of 37.9% in fiscal 2021,\ndriven by a 10.8% increase in transactions and a 4.3% increase in average ticket.\nGross profit increased 20.1% to $4.0 billion compared to $3.4 billion in fiscal 2021. As\na percentage of net sales, gross profit increased to 39.6% compared to 39.0% in fiscal\n2021, primarily due to leverage of fixed costs, strong growth in other revenue, and\nfavorable channel mix shifts, partially offset by higher inventory shrink and lower\nmerchandise margin.\nSG&A expenses increased 16.2% to $2.4 billion compared to $2.1 billion in fiscal\n2021. As a percentage of net sales, SG&A expenses decreased to 23.5% compared to\n23.9% in fiscal 2021, primarily due to lower marketing expenses and leverage of\nincentive compensation due to higher sales, partially offset by deleverage of corporate\noverhead due to strategic investments and deleverage of store payroll and benefits\ndue to wage investments.", "doc_name": "ULTABEAUTY_2023Q4_EARNINGS", "evidence_page_num": 1, "evidence_text_full_page": "Comparable sales (sales for stores open at least 14 months and e-commerce sales)\nincreased 15.6% compared to an increase of 21.4% in the fourth quarter of fiscal 2021,\ndriven by a 13.6% increase in transactions and a 1.8% increase in average ticket.\nGross profit increased 18.0% to $1.2 billion compared to $1.0 billion in the fourth\nquarter of fiscal 2021. As a percentage of net sales, gross profit of 37.6% was flat\ncompared to the fourth quarter of fiscal 2021, primarily due to leverage of fixed costs,\nfavorable channel mix shifts, and strong growth in other revenue, offset by higher\ninventory shrink.\nSelling, general and administrative (SG&A) expenses increased 17.3% to $762.7\nmillion compared to $650.0 million in the fourth quarter of fiscal 2021. As a percentage\nof net sales, SG&A expenses decreased to 23.6% compared to 23.8% in the fourth\nquarter of fiscal 2021, primarily due to leverage of marketing expenses and incentive\ncompensation due to higher sales, partially offset by deleverage of store payroll and\nbenefits due to wage investments and deleverage in corporate overhead due to\nstrategic investments.\nOperating income increased 19.2% to $447.6 million, or 13.9% of net sales, compared\nto $375.6 million, or 13.8% of net sales, in the fourth quarter of fiscal 2021.\nThe tax rate increased to 24.6% compared to 22.9% in the fourth quarter of fiscal\n2021.\nNet income increased 17.8% to $340.8 million compared to $289.4 million in the fourth\nquarter of fiscal 2021.\nDiluted earnings per share increased 23.5% to $6.68, including a $0.02 benefit due to\nincome tax accounting for stock-based compensation, compared to $5.41 including a\n$0.05 benefit due to income tax accounting for stock-based compensation, in the fourth\nquarter of fiscal 2021.\nFor the Full Year of Fiscal 2022\nNet sales increased 18.3% to $10.2 billion compared to $8.6 billion in fiscal 2021,\nprimarily due to the favorable impact from the continued resilience of the beauty\ncategory, retail price increases, the impact of new brands and product innovation,\nincreased social occasions, and fewer COVID-19 limitations compared to fiscal 2021.\nComparable sales increased 15.6% compared to an increase of 37.9% in fiscal 2021,\ndriven by a 10.8% increase in transactions and a 4.3% increase in average ticket.\nGross profit increased 20.1% to $4.0 billion compared to $3.4 billion in fiscal 2021. As\na percentage of net sales, gross profit increased to 39.6% compared to 39.0% in fiscal\n2021, primarily due to leverage of fixed costs, strong growth in other revenue, and\nfavorable channel mix shifts, partially offset by higher inventory shrink and lower\nmerchandise margin.\nSG&A expenses increased 16.2% to $2.4 billion compared to $2.1 billion in fiscal\n2021. As a percentage of net sales, SG&A expenses decreased to 23.5% compared to\n23.9% in fiscal 2021, primarily due to lower marketing expenses and leverage of\nincentive compensation due to higher sales, partially offset by deleverage of corporate\noverhead due to strategic investments and deleverage of store payroll and benefits\ndue to wage investments.\nOperating income increased 26.3% to $1.6 billion, or 16.1% of net sales, compared to\n$1.3 billion, or 15.0% of net sales, in fiscal 2021.\nThe tax rate increased to 24.4% compared to 23.9% in fiscal 2021.\nNet income increased 26.0% to $1.2 billion compared to $985.8 million in fiscal 2021.\n"}]} +{"financebench_id": "financebench_id_00603", "company": "Ulta Beauty", "doc_name": "ULTABEAUTY_2023Q4_EARNINGS", "question_type": "novel-generated", "question_reasoning": null, "domain_question_num": null, "question": "What drove the increase in Ulta Beauty's merchandise inventories balance at end of FY2023?", "answer": "Increase in Merchandise inventories balance was driven by the opening of 47 new stores. The answer here assumes FY2023 refers to the 12 months ended on January 28, 2023 (although the company refers to this period as its fiscal 2022.", "justification": "Fiscal 2022 = FY2023. Fiscal 2021 = FY2022.", "dataset_subset_label": "OPEN_SOURCE", "evidence": [{"evidence_text": "Balance Sheet\nCash and cash equivalents at the end of the fourth quarter of fiscal 2022 were $737.9\nmillion.\nMerchandise inventories, net at the end of the fourth quarter of fiscal 2022 totaled $1.6\nbillion compared to $1.5 billion at the end of the fourth quarter of fiscal 2021. The $104.2\nmillion increase was primarily due to the opening of 47 new stores since January 29, 2022,\ninventory to support new brand launches and brand expansions, and inventory cost\nincreases.", "doc_name": "ULTABEAUTY_2023Q4_EARNINGS", "evidence_page_num": 2, "evidence_text_full_page": "Diluted earnings per share increased 33.5% to $24.01, including a $0.07 benefit due to\nincome tax accounting for stock-based compensation, compared to $17.98 including a\n$0.13 benefit due to income tax accounting for stock-based compensation, in fiscal\n2021.\nBalance Sheet\nCash and cash equivalents at the end of the fourth quarter of fiscal 2022 were $737.9\nmillion.\nMerchandise inventories, net at the end of the fourth quarter of fiscal 2022 totaled $1.6\nbillion compared to $1.5 billion at the end of the fourth quarter of fiscal 2021. The $104.2\nmillion increase was primarily due to the opening of 47 new stores since January 29, 2022,\ninventory to support new brand launches and brand expansions, and inventory cost\nincreases.\nShare Repurchase Program\nDuring the fourth quarter of fiscal 2022, the Company repurchased 722,457 shares of its\ncommon stock at a cost of $328.1 million. During fiscal 2022, the Company repurchased 2.2\nmillion shares of its common stock at a cost of $900.0 million. As of January 28, 2023, $1.1\nbillion remained available under the $2.0 billion share repurchase program announced in\nMarch 2022.\nStore Update\nReal estate activity in the fourth quarter of fiscal 2022 included 12 new stores located in\nGarden Grove, CA; Glendale, AZ; Hartsdale, NY; Hollister, CA; Indianapolis, IN; Liverpool,\nNY; Nanuet, NY; Oklahoma City, OK; Richmond, TX; Rock Springs, WY; Tullahoma, TN;\nand Woburn, MA. In addition, the Company relocated one store and remodeled 12 stores.\nDuring fiscal 2022, the Company opened 47 new stores, relocated 12 stores, and remodeled\n20 stores.\nAt the end of the fourth quarter of fiscal 2022, the Company operated 1,355 stores totaling\n14.2 million square feet.\nFiscal 2023 Outlook\nFor fiscal 2023, the Company plans to:\n \n \n \n \n \nFY23 Outlook\nNet sales\n \n \n$10.95 billion to $11.05 billion\nComparable sales\n \n \n4% to 5%\nNew stores, net\n \n \n25-30\nRemodel and relocation projects\n \n \n20-30\nOperating margin\n \n \n14.7% to 15.0%\nDiluted earnings per share\n \n \n$24.70 to $25.40\nShare repurchases\n \n \napproximately $900 million\nEffective tax rate\n \n \napproximately 24.6%\nCapital expenditures\n \n \n$400 million to $475 million\nDepreciation and amortization expense\n \n \n$245 million to $250 million\n"}]} +{"financebench_id": "financebench_id_00605", "company": "Ulta Beauty", "doc_name": "ULTABEAUTY_2023Q4_EARNINGS", "question_type": "novel-generated", "question_reasoning": null, "domain_question_num": null, "question": "What percent of Ulta Beauty's total spend on stock repurchases for FY 2023 occurred in Q4 of FY2023?", "answer": "36%. The answer here assumes FY2023 refers to the 12 months ended on January 28, 2023 (although the company refers to this period as its fiscal 2022.", "justification": "Fiscal 2022 = FY2023. Fiscal 2021 = FY2022. Percent spent in Q4 of FY2023 = Amount spent in Q4 of FY2023/Total amount spent in FY2023*100 =$328.1 million /$900 million * 100 = 36%", "dataset_subset_label": "OPEN_SOURCE", "evidence": [{"evidence_text": "Share Repurchase Program\nDuring the fourth quarter of fiscal 2022, the Company repurchased 722,457 shares of its\ncommon stock at a cost of $328.1 million. During fiscal 2022, the Company repurchased 2.2\nmillion shares of its common stock at a cost of $900.0 million. As of January 28, 2023, $1.1\nbillion remained available under the $2.0 billion share repurchase program announced in\nMarch 2022.", "doc_name": "ULTABEAUTY_2023Q4_EARNINGS", "evidence_page_num": 2, "evidence_text_full_page": "Diluted earnings per share increased 33.5% to $24.01, including a $0.07 benefit due to\nincome tax accounting for stock-based compensation, compared to $17.98 including a\n$0.13 benefit due to income tax accounting for stock-based compensation, in fiscal\n2021.\nBalance Sheet\nCash and cash equivalents at the end of the fourth quarter of fiscal 2022 were $737.9\nmillion.\nMerchandise inventories, net at the end of the fourth quarter of fiscal 2022 totaled $1.6\nbillion compared to $1.5 billion at the end of the fourth quarter of fiscal 2021. The $104.2\nmillion increase was primarily due to the opening of 47 new stores since January 29, 2022,\ninventory to support new brand launches and brand expansions, and inventory cost\nincreases.\nShare Repurchase Program\nDuring the fourth quarter of fiscal 2022, the Company repurchased 722,457 shares of its\ncommon stock at a cost of $328.1 million. During fiscal 2022, the Company repurchased 2.2\nmillion shares of its common stock at a cost of $900.0 million. As of January 28, 2023, $1.1\nbillion remained available under the $2.0 billion share repurchase program announced in\nMarch 2022.\nStore Update\nReal estate activity in the fourth quarter of fiscal 2022 included 12 new stores located in\nGarden Grove, CA; Glendale, AZ; Hartsdale, NY; Hollister, CA; Indianapolis, IN; Liverpool,\nNY; Nanuet, NY; Oklahoma City, OK; Richmond, TX; Rock Springs, WY; Tullahoma, TN;\nand Woburn, MA. In addition, the Company relocated one store and remodeled 12 stores.\nDuring fiscal 2022, the Company opened 47 new stores, relocated 12 stores, and remodeled\n20 stores.\nAt the end of the fourth quarter of fiscal 2022, the Company operated 1,355 stores totaling\n14.2 million square feet.\nFiscal 2023 Outlook\nFor fiscal 2023, the Company plans to:\n \n \n \n \n \nFY23 Outlook\nNet sales\n \n \n$10.95 billion to $11.05 billion\nComparable sales\n \n \n4% to 5%\nNew stores, net\n \n \n25-30\nRemodel and relocation projects\n \n \n20-30\nOperating margin\n \n \n14.7% to 15.0%\nDiluted earnings per share\n \n \n$24.70 to $25.40\nShare repurchases\n \n \napproximately $900 million\nEffective tax rate\n \n \napproximately 24.6%\nCapital expenditures\n \n \n$400 million to $475 million\nDepreciation and amortization expense\n \n \n$245 million to $250 million\n"}]} +{"financebench_id": "financebench_id_00606", "company": "Ulta Beauty", "doc_name": "ULTABEAUTY_2023Q4_EARNINGS", "question_type": "novel-generated", "question_reasoning": null, "domain_question_num": null, "question": "Did Ulta Beauty's wages expense as a percent of net sales increase or decrease in FY2023?", "answer": "Wages expense as a percent of net sales increased in FY2023. The answer here assumes FY2023 refers to the 12 months ended on January 28, 2023 (although the company refers to this period as its fiscal 2022.", "justification": "Fiscal 2022 = FY2023. Fiscal 2021 = FY2022. Store payroll and benefits = wages. Store payroll and benefits offsets reduction in SG&A percent of net sales in FY2023.", "dataset_subset_label": "OPEN_SOURCE", "evidence": [{"evidence_text": "For the Full Year of Fiscal 2022\nNet sales increased 18.3% to $10.2 billion compared to $8.6 billion in fiscal 2021,\nprimarily due to the favorable impact from the continued resilience of the beauty\ncategory, retail price increases, the impact of new brands and product innovation,\nincreased social occasions, and fewer COVID-19 limitations compared to fiscal 2021.\nComparable sales increased 15.6% compared to an increase of 37.9% in fiscal 2021,\ndriven by a 10.8% increase in transactions and a 4.3% increase in average ticket.\nGross profit increased 20.1% to $4.0 billion compared to $3.4 billion in fiscal 2021. As\na percentage of net sales, gross profit increased to 39.6% compared to 39.0% in fiscal\n2021, primarily due to leverage of fixed costs, strong growth in other revenue, and\nfavorable channel mix shifts, partially offset by higher inventory shrink and lower\nmerchandise margin.\nSG&A expenses increased 16.2% to $2.4 billion compared to $2.1 billion in fiscal\n2021. As a percentage of net sales, SG&A expenses decreased to 23.5% compared to\n23.9% in fiscal 2021, primarily due to lower marketing expenses and leverage of\nincentive compensation due to higher sales, partially offset by deleverage of corporate\noverhead due to strategic investments and deleverage of store payroll and benefits\ndue to wage investments.", "doc_name": "ULTABEAUTY_2023Q4_EARNINGS", "evidence_page_num": 1, "evidence_text_full_page": "Comparable sales (sales for stores open at least 14 months and e-commerce sales)\nincreased 15.6% compared to an increase of 21.4% in the fourth quarter of fiscal 2021,\ndriven by a 13.6% increase in transactions and a 1.8% increase in average ticket.\nGross profit increased 18.0% to $1.2 billion compared to $1.0 billion in the fourth\nquarter of fiscal 2021. As a percentage of net sales, gross profit of 37.6% was flat\ncompared to the fourth quarter of fiscal 2021, primarily due to leverage of fixed costs,\nfavorable channel mix shifts, and strong growth in other revenue, offset by higher\ninventory shrink.\nSelling, general and administrative (SG&A) expenses increased 17.3% to $762.7\nmillion compared to $650.0 million in the fourth quarter of fiscal 2021. As a percentage\nof net sales, SG&A expenses decreased to 23.6% compared to 23.8% in the fourth\nquarter of fiscal 2021, primarily due to leverage of marketing expenses and incentive\ncompensation due to higher sales, partially offset by deleverage of store payroll and\nbenefits due to wage investments and deleverage in corporate overhead due to\nstrategic investments.\nOperating income increased 19.2% to $447.6 million, or 13.9% of net sales, compared\nto $375.6 million, or 13.8% of net sales, in the fourth quarter of fiscal 2021.\nThe tax rate increased to 24.6% compared to 22.9% in the fourth quarter of fiscal\n2021.\nNet income increased 17.8% to $340.8 million compared to $289.4 million in the fourth\nquarter of fiscal 2021.\nDiluted earnings per share increased 23.5% to $6.68, including a $0.02 benefit due to\nincome tax accounting for stock-based compensation, compared to $5.41 including a\n$0.05 benefit due to income tax accounting for stock-based compensation, in the fourth\nquarter of fiscal 2021.\nFor the Full Year of Fiscal 2022\nNet sales increased 18.3% to $10.2 billion compared to $8.6 billion in fiscal 2021,\nprimarily due to the favorable impact from the continued resilience of the beauty\ncategory, retail price increases, the impact of new brands and product innovation,\nincreased social occasions, and fewer COVID-19 limitations compared to fiscal 2021.\nComparable sales increased 15.6% compared to an increase of 37.9% in fiscal 2021,\ndriven by a 10.8% increase in transactions and a 4.3% increase in average ticket.\nGross profit increased 20.1% to $4.0 billion compared to $3.4 billion in fiscal 2021. As\na percentage of net sales, gross profit increased to 39.6% compared to 39.0% in fiscal\n2021, primarily due to leverage of fixed costs, strong growth in other revenue, and\nfavorable channel mix shifts, partially offset by higher inventory shrink and lower\nmerchandise margin.\nSG&A expenses increased 16.2% to $2.4 billion compared to $2.1 billion in fiscal\n2021. As a percentage of net sales, SG&A expenses decreased to 23.5% compared to\n23.9% in fiscal 2021, primarily due to lower marketing expenses and leverage of\nincentive compensation due to higher sales, partially offset by deleverage of corporate\noverhead due to strategic investments and deleverage of store payroll and benefits\ndue to wage investments.\nOperating income increased 26.3% to $1.6 billion, or 16.1% of net sales, compared to\n$1.3 billion, or 15.0% of net sales, in fiscal 2021.\nThe tax rate increased to 24.4% compared to 23.9% in fiscal 2021.\nNet income increased 26.0% to $1.2 billion compared to $985.8 million in fiscal 2021.\n"}]} diff --git a/RAGbenchmark/datasets/Benchmark_Lite/Financebench/pdfs/AES_2022_10K.pdf b/RAGbenchmark/datasets/Benchmark_Lite/Financebench/pdfs/AES_2022_10K.pdf new file mode 100644 index 0000000000000000000000000000000000000000..65ea6bb7e1ed0474779b0160a6f5101a47aec9c3 GIT binary patch literal 4479324 zcma&MWl$VZ+jfh)y99!}%>aYT;O_34;I6?nxI;p4*WeH!xC9CA79h9=cRM@}^S$++ zI_Jmtt7~@mUR(NF-FI~jt%`&cD}ap)9ffxKa^x>M3V?!x!qLnI9Ys)(f?dVM(Zb!_ z(uG2hM@$R^mf+ywl;9QT0)wT1Ku#_IfD8^|FE5SQc>lMt7blmZB&qfk&#u#3A`nz}ju4;8=#kP_zwfh9oz2~J)P380vm zxEL772N2_t1Oh-jQesl#eByk3QWCGTkP;V%P&BOF>?|n+IV8n_5?~NF54VIAfLDx{ ziwDHPCCMWRmXeU-k^q4?fSdpTH<%033+(P@_5T@~QxX6fnosO?e1JF)h#LgrZJRl$dz#|Ug z5SIjT@JPH~2mrsXg$K;T3z>kryP1upx!dcO^72W%_T~jiNbmu~!9Y$h4}gOk$SuXe z#{=Mdoq+^Mih~Ob0s~+7TUEv3>*AA?;L^O zg89V3;yhp}PDx2HA1C1d+>8I-QENvB2~)S%Tfzet=at}ny(Ll-VgPY5aV`MpbzwZ< z*Un;GQXp=C)axw)h;hEI3@jlAnTw*M#s5WbzW<-z!mk8N2aErv7Wlv8tKscr`I|!exq=|QMlhIpf?KWjRJb3fZiyeHwx&D0(zr>-YB3q3h0f(^G4x$ zqwu^@c-|;HZxo(43eOvb=Z(ViM&Ws*@V-%a-zdCq6y7%q?;C~pjl%mz;eDgA;Q=8$AcO~m@HinnP6&?^!sCSSAn^upLU@qW12`c(P6&?^!h?kT^$GsA zKaiLMAUOv>f)0Qr9RP_s0Fre8BVtK`T>ymzrJ)J{UGaw7yy7M0Dw3EfJgvmAs1L+T0FT@WZ#1A0E415aI_A;s+4o$7{*()*s>r5aP#c(F19J^8*O+0|@a02=N05@dF6) z0|@a02=N05@dF6)0|@a02=N05@dF6)0|@a02=N05@dF6)0|@a02=N05@dF6)0|@a0 z2=N05@dF6)0|@cswMckdAH)wJ#1A0E4Al23`pwj#1(C)HjvK|_cF)Y z%;*1VJ3j-HE!l~tNN{)?Q^0jVRWg(QxwlJyGVjd8MSS>iTcbi!jrV}6UcpNWe(sfI z%mo*q=-)w#biCRd?ayz6St(~{qu7iE>R<< zkT7{kY*DcYNzI|ueZqap>Ibp*XhtMbl-4IW>fvV^4CxD!FWc@y+xNy2P;fwxvh7KErhSQoWp)95I zSWp8CXKG!KQp4q&Ie*VbNU-OgmT@@r_Ks@yYNiWxLms^as`bSHzm=RyQiMgWzPk6% z2pO1Lx+Au%TnHF-Nf`Zb6s7wz%ASFa;A3gyi*An9(o4tUH}j)$;^6X9+bb#w!IJ^l$#s1 zq%uc&`^2+0uVg!~jZ6JK@cmzEj*#(r^eOJF=Z!14Xd%?v_Hf4}HZ1y0U*n$4Ut;(s z7riHC*33vd!x#uD&T~5dEHoFw0wQ4D8UNk;H(7Kjn^jY4>2RXVd=|^3;Zb-T>;SGf z;Jb7ey}vmC%2=||UY;{l2O4v^V8ryA=ySfC7FE66{Hc~rbNY`qK}K+O1FdvBF0{jX zlsRkN>gw)mL=1;89baQHeb8EV*=KcUM7k=2-Uv?ZVQJ3tcI%kh(<}8~s|j<~eRZ0_ z^K$`dp4zoNU4Gw+K9iVrI;0fXB z=R4Q$s(&K9e)!ViE-K8FTvd6d`cy;J6c5xPu4R0B8on8-YWs$gFej;Lrz%aW-LLqq ze)ZeN0A3SoemcU*xBclLH~h|CqSQQCk<#_4w#j{aIuu%7W+j|YkwY`M6Jwn&pxTkj zm1agLRoFik7?yvXO^2v$gGP!B5bv|PmCgva{0z7|RTbe8b6Z$WZN6RGUUXNhX#Ey$ zwYK@Awd4^0Tm0ZV0_9A#&l6z{K&GwYisv|JWxCz(x)f%sah=g3$J`eM{KJcU?pH0# z5a@_Z2DdHzPvphH$`sLa+<2@E-%j#`6_NixAX+r}()Pw&^G9ibn05t4=9${aHj{@Q z_&ps%LBoZZ(b^xMg-fHXr$C0ADq1$I3&nuynh}-z!|DoCqSB05Ks(PzvZHMGrX?>o ziQnC8>&E_l0zcb!(Mw6anEeSNsFC~>!Y9=WuS(R`^UkGqmBS?U3 zQgEhTR5-<9#M3X@cJiwN-H3DTrc)6%`W6owDO@5YvxU&Vwf7<0jQthh}gIYQJZ7GI|>uK

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