Why Can Large Language Models Generate Correct Chain-of-Thoughts?

Rasul Tutunov; Antoine Grosnit; Juliusz Ziomek; Jun Wang; Haitham Bou-Ammar

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arXiv · 2310.13571

Why Can Large Language Models Generate Correct Chain-of-Thoughts?

Rasul Tutunov·Antoine Grosnit·Juliusz Ziomek·Jun Wang·Haitham Bou-Ammar

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Abstract

This paper delves into the capabilities of large language models (LLMs), specifically focusing on advancing the theoretical comprehension of chain-of-thought prompting. We investigate how LLMs can be effectively induced to generate a coherent chain of thoughts. To achieve this, we introduce a two-level hierarchical graphical model tailored for natural language generation. Within this framework, we establish a compelling geometrical convergence rate that gauges the likelihood of an LLM-generated chain of thoughts compared to those originating from the true language. Our findings provide a theoretical justification for the ability of LLMs to produce the correct sequence of thoughts (potentially) explaining performance gains in tasks demanding reasoning skills.

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Rasul Tutunov, Antoine Grosnit, Juliusz Ziomek, Jun Wang, Haitham Bou-Ammar. 2023-10-20. Why Can Large Language Models Generate Correct Chain-of-Thoughts?. https://arxiv.org/abs/2310.13571

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