LLM, Think Before You Speak!

🌈 Abstract

The article discusses the limitations of current autoregressive Large Language Models (LLMs) and proposes a shift towards diffusive models as a potential solution to improve their reasoning capabilities. It highlights three key issues with autoregressive LLMs - sequential dependency, stochastic nature, and lack of foresight - and argues that these can be addressed by adopting a diffusive generation paradigm.

🙋 Q&A

[01] Autoregressive LLMs and their Shortcomings

1. What are the three key issues with autoregressive LLMs identified in the article?

• Sequential dependency: The words at the start of the generation are decisive for the final output and cannot be changed once generated, reducing the quality of complex outputs.
• Stochastic nature: The choice of the 'most probable' next word can lead to sub-optimal decisions early on, compounding the initial error.
• Lack of foresight: The model has no knowledge of what it will generate at the end of the sequence, which is necessary for some questions.

2. How do these issues relate to the autoregressive nature of current LLMs? The author argues that these issues are directly related to the autoregressive nature of current LLMs and can be more easily fixed by switching to a different generative paradigm, such as diffusive models.

3. How does the way autoregressive LLMs 'think' compare to the human thought process? The author notes that the way autoregressive LLMs 'think' mirrors certain aspects of the human thought process, such as the "stream-of-consciousness" or "System 1" thinking. However, this approach is not suitable for the more thoughtful and coherent process of writing, where humans typically first think before they write or have the chance to rewrite.

[02] Refining Human Thought and Reasoning

1. What are the two methods humans employ to refine their thoughts before communicating them?

• External refinement: Expressing a rough draft of thoughts and then refining them through reshuffling, deleting, appending, and rewriting.
• Internal refinement: A cognitive process of ideation, evaluation, and iterative refinement of concepts in the mind, without explicit verbalization.

2. How can these human thought refinement methods be applied to improve LLMs? The author suggests that external refinement can be implemented through "chain-of-thought" prompting, while internal refinement can be achieved through diffusive generation models, which allow the model to iteratively improve its answer before outputting the final result.

3. How can diffusive models potentially improve the reasoning capabilities of LLMs? The author cites a recent study that demonstrated improved reasoning capabilities in a diffusion model, even when compared to a much larger autoregressive model. The diffusion model also achieved a significant speed-up over classical chain-of-thought generation.

[03] Diffusion Models and the Way Forward for AI Research

1. What are the two main types of diffusion models discussed in the article?

• Explicit diffusion: Combines elements of both internal and external refinement, with intermediate written-out steps.
• Latent diffusion: Iteratively improves the internal (or latent) representation of the output, without the need for explicitly verbalized intermediary steps.

2. What are the key advantages of diffusion models over autoregressive models?

• Ability to refine the output iteratively, addressing the issues of sequential dependency, stochastic nature, and lack of foresight.
• Potential for improved reasoning capabilities, as demonstrated by recent research.
• Ability to adjust the number of refinement steps based on the complexity of the task.

3. Why does the author advocate for smaller research groups to focus on implementing original solutions, rather than incremental gains with existing architectures? The author argues that it is often unfeasible for smaller research groups to compete with the large models produced by big companies due to resource constraints. Focusing on original solutions can provide more value than incremental gains with existing architectures.