Markovian Transformers for Informative Language Modeling (2404.18988v4)

Abstract: Chain-of-Thought (CoT) reasoning holds great promise for explaining LLM outputs, but recent studies have highlighted significant challenges in its practical application for interpretability. We propose to address this issue by making CoT causally essential to prediction through two key components: factoring next-token prediction through intermediate CoT text, and training CoT to predict future tokens independently of other context. This results in "Markovian" LLMs, where CoT serves as a fixed-size state for future token prediction. Our approach optimizes for "informativeness" - the improvement in next-token predictions using a trained CoT compared to a baseline. Using Proximal Policy Optimization (PPO) for arithmetic problems and policy gradient for GSM8K, we demonstrate effectiveness on both arithmetic problems with Mistral 7B and the GSM8K benchmark with Llama 3.1 8B, where the model learns to produce CoTs that are 33.20% more effective at predicting answers than the pre-trained baseline. The increased sensitivity of model performance to CoT perturbations provides strong evidence of CoT reliance. Furthermore, we show that CoTs trained for one model generalize to help other models predict answers, suggesting these CoTs capture reasoning patterns that transfer across different interpreters. This work advances the development of more interpretable LLMs, potentially enabling their extension to arbitrarily long contexts and enhancing AI reasoning capabilities across various domains.

Exploring "Markovian Training" for LLMs' Chain-of-Thought Reasoning

Introduction to Chain-of-Thought Reasoning Challenges

The idea of using a LLM’s (LM) natural language capabilities to explain its reasoning process seems intuitive. This leads to what's known as Chain-of-Thought (CoT) prompting, where we expect the LM to provide a step-by-step explanation of its thought process before arriving at an answer. However, a key issue persists: how can we be sure that the CoT provided by the LM truly reflects its internal reasoning mechanism?

Previous studies have shown that simply changing the CoT does not always affect the final result given by the LM, suggesting that the CoT may not truly represent the LM's reasoning process. Addressing this, the paper introduces an innovative training method for LMs focused on generating meaningful and impactful CoTs that act as genuine markers of the LM's thought process.

Key Concept: Markovian LLMs and Training

Defining Markovian LLMs:

• A Markovian LM is defined as one which predicts future text, like answers to questions, using only the CoT as the context. This approach aims to ensure that the memory or state of the LM contains only tokens pertinent to future predictions, effectively transforming the CoT into a self-sufficient predictive tool.

"Markovian Training" Methodology:

• The paper proposes a novel training regimen leveraging both policy gradient and Proximal Policy Optimization (PPO) to optimize the generation of CoT tokens. This training ensures that the LM's predictions are solely based on its CoT, confirming that the CoT is integral to its reasoning process.

Empirical Validation

Achievements in Arithmetic Problem-Solving:

• The effectiveness of the Markovian training approach was evaluated on long-context arithmetic problems. The results demonstrated that the LM could utilize its generated CoTs effectively during inference sessions, confirming that these CoTs are crucial for its reasoning.

Validation of CoT's Meaningfulness:

• Beyond just utilizing CoTs for its internal processes, it was found that these generated CoTs are interpretable and transferable, meaning other models could understand and leverage them without access to the original LM's internal state. This marks significant progress in creating universally comprehensible machine reasoning steps.

Theoretical Contributions and Practical Implications

The paper emphasizes the potential for more transparent AI systems and enhances our ability to trust and understand decisions made by AI, particularly in scenarios where understanding the rationale behind a decision is as critical as the decision itself.

Future Speculations

Looking forward, the idea of solely relying on generated CoT for predictions could pave the way to more robust forms of machine reasoning where the reasoning process itself is subjected to scrutiny and improvement. This could be fundamental for applications in fields where decisions need clear justifications, like medicine or law.

In conclusion, the exploration of Markovian Training sets an exciting precedent for developing LMs that not only answer questions but provide a window into their thought process transparently and reliably.