Markovian Transformers for Informative Language Modeling (2404.18988v5)

Abstract: Chain-of-Thought (CoT) reasoning often fails to faithfully reflect a LLM's underlying decision process. We address this by making CoT text causally essential in a "Markovian" LLM, factoring next-token prediction through an intermediate CoT and training it to predict future tokens independently of the original prompt. We formalize this via an "informativeness" objective that quantifies how much a trained CoT improves next-token predictions over a baseline. Using policy gradient, we show that Llama 3.1 8B achieves a 33.2% absolute accuracy improvement on GSM8K. Perturbation tests confirm stronger reliance on the CoT, while cross-model transfers indicate these reasoning traces generalize across interpreters. Our approach enhances both accuracy and interpretability, potentially extending CoT reasoning to arbitrarily long contexts and diverse tasks.

• The paper introduces a novel Markovian training method that ensures chain-of-thought (CoT) tokens accurately represent the model’s internal reasoning.
• It employs policy gradient and Proximal Policy Optimization (PPO) to fine-tune LM predictions, specifically enhancing performance in arithmetic problem-solving.
• The study demonstrates that generated CoTs are interpretable and transferable, advancing transparent AI decision-making for critical applications.

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.