Search-Based Correction of Reasoning Chains for Language Models

• URL: http://arxiv.org/abs/2505.11824v1
• Date: Sat, 17 May 2025 04:16:36 GMT
• Title: Search-Based Correction of Reasoning Chains for Language Models
• Authors: Minsu Kim, Jean-Pierre Falet, Oliver E. Richardson, Xiaoyin Chen, Moksh Jain, Sungjin Ahn, Sungsoo Ahn, Yoshua Bengio,
• Abstract summary: Chain-of-Thought (CoT) reasoning has advanced the capabilities and transparency of language models (LMs)<n>We introduce a new self-correction framework that augments each reasoning step in a CoT with a latent variable indicating its veracity.<n>We also introduce Search Corrector, a discrete search algorithm over-valued veracity assignments.
• Score: 72.61861891295302
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Chain-of-Thought (CoT) reasoning has advanced the capabilities and transparency of language models (LMs); however, reasoning chains can contain inaccurate statements that reduce performance and trustworthiness. To address this, we introduce a new self-correction framework that augments each reasoning step in a CoT with a latent variable indicating its veracity, enabling modeling of all possible truth assignments rather than assuming correctness throughout. To efficiently explore this expanded space, we introduce Search Corrector, a discrete search algorithm over boolean-valued veracity assignments. It efficiently performs otherwise intractable inference in the posterior distribution over veracity assignments by leveraging the LM's joint likelihood over veracity and the final answer as a proxy reward. This efficient inference-time correction method facilitates supervised fine-tuning of an Amortized Corrector by providing pseudo-labels for veracity. The Amortized Corrector generalizes self-correction, enabling accurate zero-shot veracity inference in novel contexts. Empirical results demonstrate that Search Corrector reliably identifies errors in logical (ProntoQA) and mathematical reasoning (GSM8K) benchmarks. The Amortized Corrector achieves comparable zero-shot accuracy and improves final answer accuracy by up to 25%.

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