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Promises, Outlooks and Challenges of Diffusion Language Modeling

Justin Deschenaux, Caglar Gulcehre

TL;DR

This paper investigates diffusion-based text generation as an alternative to autoregressive models, focusing on Score Entropy Discrete Diffusion (SEDD). It reproduces and expands upon the main results of prior diffusion work, showing that SEDD can achieve perplexity and data-likelihood comparable to GPT-2 on standard benchmarks, while offering up to substantial latency reductions in inference. The study also highlights practical challenges, including the difficulty of caching with non-causal attention, mask-token inefficiencies, and fixed-generation lengths, and discusses directions to improve sampling efficiency and token-edit capabilities. Overall, diffusion language modeling emerges as a promising, flexible framework with competitive quality and unique advantages, although it requires further advances before it can consistently dethrone autoregressive models in practice.

Abstract

The modern autoregressive Large Language Models (LLMs) have achieved outstanding performance on NLP benchmarks, and they are deployed in the real world. However, they still suffer from limitations of the autoregressive training paradigm. For example, autoregressive token generation is notably slow and can be prone to \textit{exposure bias}. The diffusion-based language models were proposed as an alternative to autoregressive generation to address some of these limitations. We evaluate the recently proposed Score Entropy Discrete Diffusion (SEDD) approach and show it is a promising alternative to autoregressive generation but it has some short-comings too. We empirically demonstrate the advantages and challenges of SEDD, and observe that SEDD generally matches autoregressive models in perplexity and on benchmarks such as HellaSwag, Arc or WinoGrande. Additionally, we show that in terms of inference latency, SEDD can be up to 4.5$\times$ more efficient than GPT-2. While SEDD allows conditioning on tokens at abitrary positions, SEDD appears slightly weaker than GPT-2 for conditional generation given short prompts. Finally, we reproduced the main results from the original SEDD paper.

Promises, Outlooks and Challenges of Diffusion Language Modeling

TL;DR

This paper investigates diffusion-based text generation as an alternative to autoregressive models, focusing on Score Entropy Discrete Diffusion (SEDD). It reproduces and expands upon the main results of prior diffusion work, showing that SEDD can achieve perplexity and data-likelihood comparable to GPT-2 on standard benchmarks, while offering up to substantial latency reductions in inference. The study also highlights practical challenges, including the difficulty of caching with non-causal attention, mask-token inefficiencies, and fixed-generation lengths, and discusses directions to improve sampling efficiency and token-edit capabilities. Overall, diffusion language modeling emerges as a promising, flexible framework with competitive quality and unique advantages, although it requires further advances before it can consistently dethrone autoregressive models in practice.

Abstract

The modern autoregressive Large Language Models (LLMs) have achieved outstanding performance on NLP benchmarks, and they are deployed in the real world. However, they still suffer from limitations of the autoregressive training paradigm. For example, autoregressive token generation is notably slow and can be prone to \textit{exposure bias}. The diffusion-based language models were proposed as an alternative to autoregressive generation to address some of these limitations. We evaluate the recently proposed Score Entropy Discrete Diffusion (SEDD) approach and show it is a promising alternative to autoregressive generation but it has some short-comings too. We empirically demonstrate the advantages and challenges of SEDD, and observe that SEDD generally matches autoregressive models in perplexity and on benchmarks such as HellaSwag, Arc or WinoGrande. Additionally, we show that in terms of inference latency, SEDD can be up to 4.5 more efficient than GPT-2. While SEDD allows conditioning on tokens at abitrary positions, SEDD appears slightly weaker than GPT-2 for conditional generation given short prompts. Finally, we reproduced the main results from the original SEDD paper.
Paper Structure (32 sections, 1 equation, 1 figure, 7 tables)

This paper contains 32 sections, 1 equation, 1 figure, 7 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Forward and reverse processes
  4. Learning the concrete score
  5. Sampling
  6. The promises of text diffusion
  7. SEDD and GPT-2 achieve similar test likelihoods
  8. Sampling with fewer steps
  9. Simple sampling algorithm
  10. Flexibility
  11. A familiar architecture
  12. The challenges of text diffusion
  13. $Q_\text{absorb}$ only denoises once
  14. KV-caching is non-trivial
  15. Mask tokens waste FLOPs
  16. ...and 17 more sections

Figures (1)

  • Figure 1: Latency results during inference: GPT-2 (1.3B, with KV-caching) vs. SEDD (1.45B, without caching). SEDD (k): sampling from SEDD with k steps.