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Computer Science > Computation and Language

Title: Small Language Models Need Strong Verifiers to Self-Correct Reasoning

Abstract: Self-correction has emerged as a promising solution to boost the reasoning performance of large language models (LLMs), where LLMs refine their solutions using self-generated critiques that pinpoint the errors. This work explores whether smaller-size (<= 13B) language models (LMs) have the ability of self-correction on reasoning tasks with minimal inputs from stronger LMs. We propose a novel pipeline that prompts smaller LMs to collect self-correction data that supports the training of self-refinement abilities. First, we leverage correct solutions to guide the model in critiquing their incorrect responses. Second, the generated critiques, after filtering, are used for supervised fine-tuning of the self-correcting reasoner through solution refinement. Our experimental results show improved self-correction abilities of two models on five datasets spanning math and commonsense reasoning, with notable performance gains when paired with a strong GPT-4-based verifier, though limitations are identified when using a weak self-verifier for determining when to correct.
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2404.17140 [cs.CL]
  (or arXiv:2404.17140v1 [cs.CL] for this version)

Submission history

From: Yunxiang Zhang [view email]
[v1] Fri, 26 Apr 2024 03:41:28 GMT (440kb,D)

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