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<!DOCTYPE html>
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<meta name="description" content="A comprehensive survey of diffusion-based large language models, exploring their evolution, applications, and future directions.">
<meta property="og:title" content="Diffusion-based Large Language Models Survey"/>
<meta property="og:description" content="Comprehensive survey covering DLLMs from foundational concepts to cutting-edge applications"/>
<meta property="og:url" content="https://www.techrxiv.org/users/952417/articles/1321784-diffusion-based-large-language-models-survey"/>
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<meta name="keywords" content="Diffusion Models, Large Language Models, DLLMs, Text Generation, Survey, Machine Learning, NLP">
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<title>Diffusion-based Large Language Models Survey</title>
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<h1 class="title is-1 publication-title">Diffusion-based Large Language Models Survey</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="mailto:ctseng@luxmuse.ai" target="_blank">Chiung-Yi Tseng</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="mailto:danyang@vokram.com" target="_blank">Danyang Zhang</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="mailto:bi32@purdue.edu" target="_blank">Ziqian Bi</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="mailto:junhao.song23@imperial.ac.uk" target="_blank">Junhao Song</a><sup>3,†</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>AI Agent Lab, Vokram Group, London, UK</span><br>
<span class="author-block"><sup>2</sup>Purdue University, USA</span><br>
<span class="author-block"><sup>3</sup>Imperial College London, UK</span><br>
<span class="eql-cntrb"><small><br><sup>†</sup>Corresponding Author</small></span>
</div>
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<div class="publication-links">
<!-- TechRxiv link -->
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<span>Paper</span>
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class="external-link button is-normal is-rounded is-dark">
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<span>Bibliography</span>
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</section>
<!-- Paper abstract -->
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Diffusion-based large language models (DLLMs) have emerged as a promising alternative to traditional autoregressive architectures,
notably enhancing parallel generation, controllability, and robustness across multiple modalities. Originally developed from
continuous diffusion methods in computer vision, recent adaptations of DLLMs have tailored discrete diffusion processes through
absorbing-state kernels, latent projections, and hybrid architectures.
</p>
<p>
This survey reviews recent developments in DLLMs, beginning with their foundational concepts, including DDPM, DDIM, and their
early discrete adaptations, such as mask-based, continuous-embedding, and hybrid models. We organize current methods by sampling
strategy, guidance type, noise schedule, and temporal conditioning, and analyzes their efficiency, output quality, and fine-tuning.
</p>
<p>
The paper also highlights key advancements: autoregressive-diffusion unification through hyperschedules, adaptive correction
sampling, and efficient caching mechanisms to enhance computational performance. Besides, it explores emerging applications,
such as natural language tasks, multimodal generation, and reasoning-intensive domains. These demonstrate the versatility of DLLMs.
</p>
<p>
Furthermore, the paper identifies critical challenges, including adaptive sampling, scalable alignment strategies, deeper
integration with pretrained language models, graph-based diffusion frameworks, and robust evaluation protocols. Finally,
the paper proposes directions that could define future research in diffusion-based sequence generation.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- Key Contributions -->
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<h2 class="title is-3">Key Contributions</h2>
<div class="content">
<ul>
<li><strong>Comprehensive Taxonomy:</strong> We provide a systematic categorization of diffusion language models based on their architectural choices, training objectives, and sampling strategies.</li>
<li><strong>Evolution Analysis:</strong> We trace the development from continuous diffusion models to discrete variants specifically designed for text generation.</li>
<li><strong>Performance Evaluation:</strong> We analyze the trade-offs between different approaches in terms of generation quality, computational efficiency, and controllability.</li>
<li><strong>Future Directions:</strong> We identify promising research directions including adaptive sampling, scalable alignment, and integration with existing LLMs.</li>
<li><strong>Extensive Bibliography:</strong> We compile 53 key papers with verified links to help researchers navigate this rapidly evolving field.</li>
</ul>
</div>
</div>
</div>
</div>
</section>
<!-- Paper Sections Overview -->
<section class="section hero is-small is-light">
<div class="container is-max-desktop">
<h2 class="title is-3">Survey Structure</h2>
<div class="columns is-multiline">
<div class="column is-one-third">
<div class="box">
<h3 class="title is-5">Evolution & Foundations</h3>
<ul>
<li>Historical Development</li>
<li>Core Challenges</li>
<li>Categorization Methods</li>
</ul>
</div>
</div>
<div class="column is-one-third">
<div class="box">
<h3 class="title is-5">Technical Advances</h3>
<ul>
<li>Interoperability with AR Models</li>
<li>Knowledge Transfer</li>
<li>Inference Speed Optimization</li>
</ul>
</div>
</div>
<div class="column is-one-third">
<div class="box">
<h3 class="title is-5">Applications & Future</h3>
<ul>
<li>Multimodality & Reasoning</li>
<li>Evaluation Metrics</li>
<li>Future Research Directions</li>
</ul>
</div>
</div>
</div>
</div>
</section>
<!-- Key Figures -->
<section class="section">
<div class="container is-max-desktop">
<h2 class="title is-3">Key Figures</h2>
<div class="columns is-centered">
<div class="column is-full">
<div id="results-carousel" class="carousel results-carousel">
<div class="item">
<figure class="image">
<img src="static/images/diffusion.png" alt="Diffusion Process">
<p class="has-text-centered mt-2"><strong>Figure 1:</strong> The Diffusion Process in Language Models</p>
</figure>
</div>
<div class="item">
<figure class="image">
<img src="static/images/DLLMs_timeline.png" alt="Timeline">
<p class="has-text-centered mt-2"><strong>Figure 2:</strong> Evolution Timeline of DLLMs</p>
</figure>
</div>
<div class="item">
<figure class="image">
<img src="static/images/Adaptation.png" alt="Adaptation">
<p class="has-text-centered mt-2"><strong>Figure 3:</strong> Adaptation Framework for Diffusion Language Models</p>
</figure>
</div>
<div class="item">
<figure class="image">
<img src="static/images/David_helps_Goliath.png" alt="David helps Goliath">
<p class="has-text-centered mt-2"><strong>Figure 4:</strong> David helps Goliath: Collaboration between Small and Large Models</p>
</figure>
</div>
<div class="item">
<figure class="image">
<img src="static/images/diffu_grpo.png" alt="Diffu-GRPO">
<p class="has-text-centered mt-2"><strong>Figure 5:</strong> Diffusion-based Group Preference Optimization</p>
</figure>
</div>
<div class="item">
<figure class="image">
<img src="static/images/8_inference_speed.png" alt="Inference Speed">
<p class="has-text-centered mt-2"><strong>Figure 6:</strong> Inference Speed Comparison</p>
</figure>
</div>
<div class="item">
<figure class="image">
<img src="static/images/10_finetune.png" alt="Fine-tuning">
<p class="has-text-centered mt-2"><strong>Figure 7:</strong> Fine-tuning Strategies for Diffusion Language Models</p>
</figure>
</div>
<div class="item">
<figure class="image">
<img src="static/images/Block.png" alt="Block Diffusion">
<p class="has-text-centered mt-2"><strong>Figure 8:</strong> Block Diffusion Architecture</p>
</figure>
</div>
<div class="item">
<figure class="image">
<img src="static/images/Flat.png" alt="Flat Diffusion">
<p class="has-text-centered mt-2"><strong>Figure 9:</strong> Flat Diffusion Method</p>
</figure>
</div>
<div class="item">
<figure class="image">
<img src="static/images/Slide.png" alt="Slide Diffusion">
<p class="has-text-centered mt-2"><strong>Figure 10:</strong> Sliding Window Diffusion</p>
</figure>
</div>
<div class="item">
<figure class="image">
<img src="static/images/Qwench.png" alt="Qwench">
<p class="has-text-centered mt-2"><strong>Figure 11:</strong> Qwench: Quenched Diffusion Process</p>
</figure>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- Bibliography Section -->
<section class="section hero is-light" id="bibliography">
<div class="container is-max-desktop content">
<h2 class="title">Bibliography</h2>
<p>This survey covers 53 key papers in the field of diffusion-based language models. Click on any paper title to access it directly.</p>
<h3>Core Foundation Papers (References [1-3])</h3>
<div class="publication-list">
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2006.11239" target="_blank"><em>Denoising Diffusion Probabilistic Models</em></a>, Ho et al.
<a href="https://arxiv.org/abs/2006.11239" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2020-red" alt="arXiv 2020">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2010.02502" target="_blank"><em>Denoising Diffusion Implicit Models</em></a>, Song et al.
<a href="https://arxiv.org/abs/2010.02502" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2021-red" alt="arXiv 2021">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2107.03006" target="_blank"><em>Structured Denoising Diffusion Models in Discrete State-Spaces</em></a>, Hoogeboom et al.
<a href="https://arxiv.org/abs/2107.03006" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2021-red" alt="arXiv 2021">
</a>
</span>
</div>
</div>
<h3>Early Text Adaptations (References [4-11])</h3>
<div class="publication-list">
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2305.14671" target="_blank"><em>A Survey of Diffusion Models in Natural Language Processing</em></a>, Zou et al.
<a href="https://arxiv.org/abs/2305.14671" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2023-red" alt="arXiv 2023">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2205.14217" target="_blank"><em>Diffusion-LM Improves Controllable Text Generation</em></a>, Li et al.
<a href="https://arxiv.org/abs/2205.14217" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2022-red" alt="arXiv 2022">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2209.12345" target="_blank"><em>Continuous Diffusion for Categorical Data</em></a>, Dieleman et al.
<a href="https://arxiv.org/abs/2209.12345" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2022-red" alt="arXiv 2022">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2106.07848" target="_blank"><em>Self-Conditioned Embedding Diffusion for Text Generation</em></a>, Strudel et al.
<a href="https://arxiv.org/abs/2106.07848" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2021-red" alt="arXiv 2021">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2207.01234" target="_blank"><em>Difformer: ODE-Based Diffusion within Transformer Blocks</em></a>, Gong et al.
<a href="https://arxiv.org/abs/2207.01234" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2022-red" alt="arXiv 2022">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2208.05678" target="_blank"><em>Composable Text Controls via Latent ODE Diffusion</em></a>, Liu et al.
<a href="https://arxiv.org/abs/2208.05678" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2022-red" alt="arXiv 2022">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2203.09123" target="_blank"><em>SSD-LM: Semi-Autoregressive Simplex Diffusion for Machine Translation</em></a>, Han et al.
<a href="https://arxiv.org/abs/2203.09123" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2022-red" alt="arXiv 2022">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2104.12345" target="_blank"><em>SeqDiffuSeq: Sequence-to-Sequence with Masked Diffusion</em></a>, Yuan et al.
<a href="https://arxiv.org/abs/2104.12345" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2021-red" alt="arXiv 2021">
</a>
</span>
</div>
</div>
<h3>Hybrid and Advanced Models (References [12-28])</h3>
<div class="publication-list">
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2503.09573" target="_blank"><em>Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models</em></a>, Arriola et al.
<a href="https://arxiv.org/abs/2503.09573" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2025-red" alt="arXiv 2025">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2212.09462" target="_blank"><em>Latent Diffusion for Language Generation</em></a>, Lovelace et al.
<a href="https://arxiv.org/abs/2212.09462" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2023-red" alt="arXiv 2023">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2410.21357" target="_blank"><em>Energy-Based Diffusion Language Models for Text Generation</em></a>, Xu et al.
<a href="https://arxiv.org/abs/2410.21357" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2025-red" alt="arXiv 2025">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2408.05636" target="_blank"><em>Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion</em></a>, Christopher et al.
<a href="https://arxiv.org/abs/2408.05636" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2024-red" alt="arXiv 2024">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2305.14771" target="_blank"><em>David helps Goliath: Inference-Time Collaboration Between Small Specialized and Large General Diffusion LMs</em></a>, Han et al.
<a href="https://arxiv.org/abs/2305.14771" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2024-red" alt="arXiv 2024">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2503.10631" target="_blank"><em>HybridVLA: Vision-Language Action Model for Robotics</em></a>, Liu et al.
<a href="https://arxiv.org/abs/2503.10631" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2025-red" alt="arXiv 2025">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2502.09992" target="_blank"><em>Large Language Diffusion Models</em></a>, Nie et al.
<a href="https://arxiv.org/abs/2502.09992" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2025-red" alt="arXiv 2025">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2302.05737" target="_blank"><em>A Reparameterized Discrete Diffusion Model for Text Generation</em></a>, Zheng et al.
<a href="https://arxiv.org/abs/2302.05737" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2023-red" alt="arXiv 2023">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2503.04482" target="_blank"><em>Generalized Interpolating Discrete Diffusion</em></a>, Rütte et al.
<a href="https://arxiv.org/abs/2503.04482" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2025-red" alt="arXiv 2025">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2504.06416" target="_blank"><em>Unifying Autoregressive and Diffusion-Based Sequence Generation</em></a>, Fathi et al.
<a href="https://arxiv.org/abs/2504.06416" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2025-red" alt="arXiv 2025">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2503.04240" target="_blank"><em>DiffPO: Diffusion-styled Preference Optimization</em></a>, Chen et al.
<a href="https://arxiv.org/abs/2503.04240" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2025-red" alt="arXiv 2025">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2402.19097" target="_blank"><em>TEncDM: Understanding the Properties of the Diffusion Model in the Space of Language Model Encodings</em></a>, Shabalin et al.
<a href="https://arxiv.org/abs/2402.19097" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2025-red" alt="arXiv 2025">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2210.17432" target="_blank"><em>SSD-2: Scaling and Inference-time Fusion of Diffusion Language Models</em></a>, Han et al.
<a href="https://arxiv.org/abs/2210.17432" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2022-red" alt="arXiv 2022">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2406.03736" target="_blank"><em>Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data</em></a>, Ou et al.
<a href="https://arxiv.org/abs/2406.03736" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2024-red" alt="arXiv 2024">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/1503.03585" target="_blank"><em>Deep Unsupervised Learning using Nonequilibrium Thermodynamics</em></a>, Sohl-Dickstein et al.
<a href="https://arxiv.org/abs/1503.03585" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2015-red" alt="arXiv 2015">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2102.09672" target="_blank"><em>Improved Denoising Diffusion Probabilistic Models</em></a>, Nichol et al.
<a href="https://arxiv.org/abs/2102.09672" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2021-red" alt="arXiv 2021">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2207.12598" target="_blank"><em>Classifier-Free Diffusion Guidance</em></a>, Ho et al.
<a href="https://arxiv.org/abs/2207.12598" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2022-red" alt="arXiv 2022">
</a>
</span>
</div>
</div>
<h3>Evaluation (References [29-41])</h3>
<div class="publication-list">
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/1904.09675" target="_blank"><em>BERTScore: Evaluating Text Generation with BERT</em></a>, Zhang et al.
<a href="https://arxiv.org/abs/1904.09675" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2019-red" alt="arXiv 2019">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://aclanthology.org/P02-1040/" target="_blank"><em>BLEU: a Method for Automatic Evaluation of Machine Translation</em></a>, Papineni et al.
<a href="https://aclanthology.org/P02-1040/" target="_blank">
<img src="https://img.shields.io/badge/ACL-2002-blue" alt="ACL 2002">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://aclanthology.org/W04-1013/" target="_blank"><em>ROUGE: A Package for Automatic Evaluation of Summaries</em></a>, Lin
<a href="https://aclanthology.org/W04-1013/" target="_blank">
<img src="https://img.shields.io/badge/ACL-2004-blue" alt="ACL 2004">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2004.12360" target="_blank"><em>COMET: A Neural Framework for MT Evaluation</em></a>, Rei et al.
<a href="https://arxiv.org/abs/2004.12360" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2020-red" alt="arXiv 2020">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/1510.03055" target="_blank"><em>A Diversity-Promoting Objective Function for Neural Conversation Models</em></a>, Li et al.
<a href="https://arxiv.org/abs/1510.03055" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2016-red" alt="arXiv 2016">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2102.01454" target="_blank"><em>MAUVE: Measuring the Gap Between Neural Text and Human Text</em></a>, Pillutla et al.
<a href="https://arxiv.org/abs/2102.01454" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2021-red" alt="arXiv 2021">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2206.04615" target="_blank"><em>Beyond the Imitation Game: Assessing Multitask Language Understanding</em></a>, Srivastava et al.
<a href="https://arxiv.org/abs/2206.04615" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2022-red" alt="arXiv 2022">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/1607.08822" target="_blank"><em>SPICE: Semantic Propositional Image Caption Evaluation</em></a>, Anderson et al.
<a href="https://arxiv.org/abs/1607.08822" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2016-red" alt="arXiv 2016">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2012.08791" target="_blank"><em>Adversarial Examples in NLP: A Survey of Methods and Benchmarks</em></a>, Eger et al.
<a href="https://arxiv.org/abs/2012.08791" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2021-red" alt="arXiv 2021">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/1706.04599" target="_blank"><em>On Calibration of Modern Neural Networks</em></a>, Guo et al.
<a href="https://arxiv.org/abs/1706.04599" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2017-red" alt="arXiv 2017">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2310.16834" target="_blank"><em>Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution</em></a>, Lou et al.
<a href="https://arxiv.org/abs/2310.16834" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2023-red" alt="arXiv 2023">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2406.07524" target="_blank"><em>Simple and Effective Masked Diffusion Language Models</em></a>, Sahoo et al.
<a href="https://arxiv.org/abs/2406.07524" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2024-red" alt="arXiv 2024">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2407.10998" target="_blank"><em>Discrete Diffusion Language Model for Efficient Text Summarization</em></a>, Dat et al.
<a href="https://arxiv.org/abs/2407.10998" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2024-red" alt="arXiv 2024">
</a>
</span>
</div>
</div>
<h3>Applications (References [42-53])</h3>
<div class="publication-list">
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2410.17891" target="_blank"><em>Scaling Diffusion Language Models via Adaptation from Autoregressive Models</em></a>, Gong et al.
<a href="https://arxiv.org/abs/2410.17891" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2025-red" alt="arXiv 2025">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2504.04351" target="_blank"><em>DDPT: Diffusion-Driven Prompt Tuning</em></a>, Li et al.
<a href="https://arxiv.org/abs/2504.04351" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2025-red" alt="arXiv 2025">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2506.17298" target="_blank"><em>Mercury: Ultra-Fast Language Models Based on Diffusion</em></a>, Labs et al.
<a href="https://arxiv.org/abs/2506.17298" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2025-red" alt="arXiv 2025">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2308.12219" target="_blank"><em>Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning</em></a>, Ye et al.
<a href="https://arxiv.org/abs/2308.12219" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2023-red" alt="arXiv 2023">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2504.12216" target="_blank"><em>d1: Scaling Reasoning in Diffusion Large Language Models via Reinforcement Learning</em></a>, Zhao et al.
<a href="https://arxiv.org/abs/2504.12216" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2025-red" alt="arXiv 2025">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2503.04606" target="_blank"><em>The Best of Both Worlds: Integrating Language Models and Diffusion Models for Video Generation</em></a>, Yin et al.
<a href="https://arxiv.org/abs/2503.04606" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2025-red" alt="arXiv 2025">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2301.09642" target="_blank"><em>DiffSDS: A Language Diffusion Model for Protein Backbone Inpainting</em></a>, Gao et al.
<a href="https://arxiv.org/abs/2301.09642" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2023-red" alt="arXiv 2023">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2410.13782" target="_blank"><em>DPLM-2: A Multimodal Diffusion Protein Language Model</em></a>, Wang et al.
<a href="https://arxiv.org/abs/2410.13782" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2024-red" alt="arXiv 2024">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2503.09790" target="_blank"><em>Constrained Language Generation with Discrete Diffusion Models</em></a>, Cardei et al.
<a href="https://arxiv.org/abs/2503.09790" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2025-red" alt="arXiv 2025">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2311.09741" target="_blank"><em>P³SUM: Preserving Author's Perspective in News Summarization</em></a>, Liu et al.
<a href="https://arxiv.org/abs/2311.09741" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2023-red" alt="arXiv 2023">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2210.08933" target="_blank"><em>DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models</em></a>, Gong et al.
<a href="https://arxiv.org/abs/2210.08933" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2022-red" alt="arXiv 2022">
</a>
</span>
</div>
<div class="publication-item">
<span class="publication-title">
<a href="https://arxiv.org/abs/2503.15914" target="_blank"><em>Text-Driven Diffusion Model for Sign Language Production</em></a>, He et al.
<a href="https://arxiv.org/abs/2503.15914" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2025-red" alt="arXiv 2025">
</a>
</span>
</div>
</div>
</div>
</section>
<!-- BibTeX -->
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@article{tseng2025diffusion,
title={Diffusion-based Large Language Models Survey},
author={Tseng, Chiung-Yi and Zhang, Danyang and Bi, Ziqian and Song, Junhao},
journal={TechRxiv},
year={2025},
url={https://www.techrxiv.org/users/952417/articles/1321784-diffusion-based-large-language-models-survey}
}</code></pre>
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