Evaluating Language Models with lighteval

This repository provides tools and guidance for evaluating and comparing language models (LLMs) across a wide range of tasks and benchmarks. Evaluation is essential for understanding model performance, identifying strengths and weaknesses, and ensuring alignment with specific use cases. Using lighteval, this repository demonstrates how to perform standard benchmark evaluations as well as create custom evaluation pipelines tailored to domain-specific requirements.

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Repository Contents

Automatic Benchmarks

This section focuses on evaluating LLMs using standardized benchmarks like MMLU, TruthfulQA, and BBH. These benchmarks assess model capabilities such as:

• General knowledge and reasoning.
• Logical thinking and problem-solving.
• Language understanding and common sense.

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Notebooks

The repository includes interactive notebooks for hands-on exploration of lighteval:

Evaluating and Analyzing LLMs

• Description: Demonstrates how to use lighteval to evaluate and compare LLMs on various benchmarks and tasks.
• What's Inside:
  • Evaluation of two models (Qwen2.5-0.5B and SmolLM2-360M-Instruct) on medical domain tasks.
  • Setup and configuration of evaluation pipelines.
  • Visualization and analysis of results.
• Notebook: Evaluate and Analyze Your LLM

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Why Use lighteval?

lighteval simplifies the evaluation process by providing:

• Task Flexibility: Define evaluation tasks using a structured format that supports benchmarks like MMLU and custom tasks.
• Python Integration: Seamlessly integrate evaluation into Python workflows for automated and programmatic analysis.
• Efficient Processing: Evaluate models with minimal setup, making it ideal for quick experimentation and benchmarking.

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Getting Started

1. Run Automatic Benchmarks: Start by evaluating your model on standard benchmarks to establish a baseline.

   "leaderboard|mmlu:anatomy|5|0,leaderboard|mmlu:professional_medicine|5|0"

2. Create Custom Domain Evaluations: Define tasks and datasets specific to your application. For example:

   [
       "mmlu|anatomy|0|0",
       "mmlu|high_school_biology|0|0",
       "mmlu|high_school_chemistry|0|0",
       "mmlu|professional_medicine|0|0"
   ]

3. Visualize Results: Analyze model performance with side-by-side comparisons, detailed metrics, and flexible visualization options.