Likelihood-Based Diffusion Language Models (2305.18619v1)

Abstract: Despite a growing interest in diffusion-based LLMs, existing work has not shown that these models can attain nontrivial likelihoods on standard LLMing benchmarks. In this work, we take the first steps towards closing the likelihood gap between autoregressive and diffusion-based LLMs, with the goal of building and releasing a diffusion model which outperforms a small but widely-known autoregressive model. We pursue this goal through algorithmic improvements, scaling laws, and increased compute. On the algorithmic front, we introduce several methodological improvements for the maximum-likelihood training of diffusion LLMs. We then study scaling laws for our diffusion models and find compute-optimal training regimes which differ substantially from autoregressive models. Using our methods and scaling analysis, we train and release Plaid 1B, a large diffusion LLM which outperforms GPT-2 124M in likelihood on benchmark datasets and generates fluent samples in unconditional and zero-shot control settings.

• The paper introduces the Plaid framework, significantly enhancing diffusion language models with learned embeddings and categorical reparameterization.
• It establishes compute-optimal scaling laws that enable diffusion models to outperform autoregressive models like GPT-2 124M.
• The Plaid 1B model achieves superior zero-shot likelihood and fluency across multiple benchmarks, demonstrating practical advantages in controllable text generation.

Overview of "Likelihood-Based Diffusion LLMs"

The research presented in the paper titled "Likelihood-Based Diffusion LLMs" explores the potential of diffusion models in the context of LLMing, with a focus on achieving competitive likelihoods on standard benchmarks. Authored by Gulrajani and Hashimoto, the work critically addresses the existing gap in likelihood performance between traditional autoregressive LLMs and diffusion LLMs, which have achieved notable success in the image domain.

Contributions and Methodological Advances

This research introduces several significant contributions to the diffusion model paradigm:

1. Algorithmic Framework: Plaid - The authors propose an algorithmic framework, named Plaid, to enhance the performance of diffusion LLMs. This framework incorporates a series of methodological innovations such as learned embeddings, categorical reparameterization, and a comprehensive adaptation of the Variational Diffusion Models (VDM) framework for language.
2. Scaling and Training Dynamics - A core aspect of this work is the development and analysis of scaling laws. The research identifies compute-optimal training regimes that differ substantially from those used in autoregressive models. By leveraging these insights, Plaid achieves better likelihood performance than GPT-2 124M, a commonly referenced autoregressive model.
3. Release of Plaid 1B Model - Utilizing their framework and scaling law insights, the authors train and release the Plaid 1B model. This large diffusion LLM not only outperforms GPT-2 124M across multiple benchmark datasets in zero-shot likelihood settings but also demonstrates capabilities in fluent and controllable text generation.

Numerical Results and Evaluations

The paper reports strong numerical results that include:

• Likelihood Gains - Plaid 1B exhibits superior zero-shot likelihood across six benchmarks, indicating its effectiveness in reducing the autoregressive and diffusion model performance gap.
• Scaling Laws Validation - Through an IsoFLOP analysis, the authors demonstrate that Plaid models improve predictably with compute at a similar rate to autoregressive models, albeit with differences in compute efficiency and optimal parameter settings.
• Ablation Experiments - Detailed ablation studies validate the individual contributions of various algorithmic components, confirming the impacts on log-likelihood improvements.

Implications and Future Directions

The research's implications are twofold:

1. Practical Implications - The outcomes suggest that diffusion models are a promising alternative to autoregressive models, particularly for tasks that benefit from the inherent advantages of the diffusion paradigm, such as parallelizable generation and controllable text synthesis.
2. Theoretical Contributions - The extension of VDM to LLMing along with insights into compute-optimal model scaling enrich the theoretical foundation of diffusion models in AI.

Looking forward, the work opens several avenues for exploration:

• Improved Efficiency - Further research is warranted to address the current efficiency gap relative to autoregressive models. This may involve continued algorithmic refinements and hardware-specific optimizations.
• Broader AI Applications - The principles established here could extend to other generative modeling tasks beyond language, such as symbolic reasoning or hybrid multimedia generation.

Overall, this work signifies an important step in the maturation of diffusion models for language tasks and sets a foundation for ongoing advancements in AI LLMing.