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

• URL: http://arxiv.org/abs/2310.13571v4
• Date: Thu, 6 Jun 2024 12:18:56 GMT
• Title: Why Can Large Language Models Generate Correct Chain-of-Thoughts?
• Authors: Rasul Tutunov, Antoine Grosnit, Juliusz Ziomek, Jun Wang, Haitham Bou-Ammar,
• Abstract summary: We introduce a two-level hierarchical graphical model tailored for natural language generation. We establish a compelling geometrical convergence rate that gauges the likelihood of an LLM-generated chain of thoughts.
• Score: 10.888196404348093
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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