Hint Marginalization for Improved Reasoning in Large Language Models

• URL: http://arxiv.org/abs/2412.13292v1
• Date: Tue, 17 Dec 2024 19:45:53 GMT
• Title: Hint Marginalization for Improved Reasoning in Large Language Models
• Authors: Soumyasundar Pal, Didier Chételat, Yingxue Zhang, Mark Coates,
• Abstract summary: We present Marginalization, a novel and principled algorithmic framework to enhance the reasoning capabilities of Large Language Models (LLMs) Our approach can be viewed as an iterative sampling strategy for forming a Monte Carlo approximation of an underlying distribution of answers. Empirical evaluation on several benchmark datasets for arithmetic reasoning demonstrates the superiority of the proposed approach.
• Score: 24.67507932821155
• License:
• Abstract: Large Language Models (LLMs) have exhibited an impressive capability to perform reasoning tasks, especially if they are encouraged to generate a sequence of intermediate steps. Reasoning performance can be improved by suitably combining multiple LLM responses, generated either in parallel in a single query, or via sequential interactions with LLMs throughout the reasoning process. Existing strategies for combination, such as self-consistency and progressive-hint-prompting, make inefficient usage of the LLM responses. We present Hint Marginalization, a novel and principled algorithmic framework to enhance the reasoning capabilities of LLMs. Our approach can be viewed as an iterative sampling strategy for forming a Monte Carlo approximation of an underlying distribution of answers, with the goal of identifying the mode the most likely answer. Empirical evaluation on several benchmark datasets for arithmetic reasoning demonstrates the superiority of the proposed approach.

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