Energy-Based Diffusion Language Models for Text Generation (2410.21357v4)

Abstract: Despite remarkable progress in autoregressive LLMs, alternative generative paradigms beyond left-to-right generation are still being actively explored. Discrete diffusion models, with the capacity for parallel generation, have recently emerged as a promising alternative. Unfortunately, these models still underperform the autoregressive counterparts, with the performance gap increasing when reducing the number of sampling steps. Our analysis reveals that this degradation is a consequence of an imperfect approximation used by diffusion models. In this work, we propose Energy-based Diffusion LLM (EDLM), an energy-based model operating at the full sequence level for each diffusion step, introduced to improve the underlying approximation used by diffusion models. More specifically, we introduce an EBM in a residual form, and show that its parameters can be obtained by leveraging a pretrained autoregressive model or by finetuning a bidirectional transformer via noise contrastive estimation. We also propose an efficient generation algorithm via parallel important sampling. Comprehensive experiments on LLMing benchmarks show that our model can consistently outperform state-of-the-art diffusion models by a significant margin, and approaches autoregressive models' perplexity. We further show that, without any generation performance drop, our framework offers a 1.3$\times$ sampling speedup over existing diffusion models. Reproduced code is available at https://github.com/MinkaiXu/Energy-Diffusion-LLM.

• The paper proposes an Energy-based Diffusion Language Model that integrates sequence-level energy parameterization to enhance token prediction accuracy.
• It employs parallel importance sampling to achieve a significant 1.3× speedup, reducing the computational demands of conventional diffusion methods.
• Empirical evaluations demonstrate that EDLM attains competitive perplexity scores compared to state-of-the-art autoregressive models.

An Expert Review of "Energy-Based Diffusion LLMs for Text Generation"

The paper entitled "Energy-Based Diffusion LLMs for Text Generation" presents an innovative approach to address inherent limitations in discrete diffusion models for natural language generation. By augmenting diffusion models with energy-based modeling techniques, the authors propose a novel framework, named Energy-based Diffusion LLM (EDLM), aimed at enhancing the efficacy of token sequence predictions during the language generation process.

Overview and Key Contributions

The paper begins by recognizing the limitations of existing autoregressive (AR) models and various alternative generative paradigms, especially in their inflexibility and exposure bias. Despite advancements, discrete diffusion models have not surpassed AR models, partly due to the discrepancy in training and decoherence of the sampling distribution. The core insight of this paper lies in the realization that diffusion models suffer from a mismatch between their training and sampling distributions, mainly due to their independent token-wise predictions which disregard sequence-level correlations.

To mitigate these shortcomings, the authors introduce an energy-based model that operates across the entire sequence rather than at individual token levels. The proposed EDLM achieves this by integrating a residual form energy parameterization over pretrained diffusion models. This methodology introduces the use of pretrained AR models or bidirectional transformers as energy functions. Notably, EDLM facilitates greater sampling efficiency via parallel importance sampling, a key innovation that addresses the traditionally high computational demands of diffusion models.

The authors substantiate their claims with rigorous empirical evaluations across several language generation benchmarks. The experimental results demonstrate that EDLM consistently outperforms existing state-of-the-art diffusion models, achieves competitive perplexity scores close to autoregressive models, and critically, offers a significant 1.3× speedup in sampling time over traditional diffusion-based approaches.

Implications and Future Directions

The introduction of an energy-based framework to augment diffusion LLMs presents significant implications for the field of text generation. By effectively marrying the strengths of energy-based modeling with diffusion processes, the approach offers new pathways to address the sampling accuracy and efficiency challenges that have long hindered the adoption of diffusion models.

Moreover, the paper opens several avenues for future research. Firstly, there remains a potential to further optimize the energy parameterization for even greater sampling speedups. Another promising direction could involve exploring the integration of this framework with other generative paradigms, such as variational autoencoders or generative adversarial networks. Furthermore, adapting the methodology for multi-modal generative tasks could see widespread application in environments where coherent cross-modal generation (e.g., text-to-image) is critical.

Conclusion

The Energy-Based Diffusion LLM presented in this paper represents a substantial innovation within text generation methodologies. By addressing the historic limitations of discrete diffusion models through energy-based techniques, the authors not only provide a competitive alternative to autoregressive LLMs but also set a transformative precedent for future research in efficient and effective parallel text generation solutions.