Self-Training Meets Consistency: Improving LLMs' Reasoning With Consistency-Driven Rationale Evaluation

• URL: http://arxiv.org/abs/2411.06387v2
• Date: Fri, 22 Nov 2024 08:54:17 GMT
• Title: Self-Training Meets Consistency: Improving LLMs' Reasoning With Consistency-Driven Rationale Evaluation
• Authors: Jaehyeok Lee, Keisuke Sakaguchi, JinYeong Bak,
• Abstract summary: Self-training approach for large language models (LLMs) improves reasoning abilities by training the models on their self-generated rationales. Previous approaches have labeled rationales that produce correct answers for a given question as appropriate for training. We propose CREST (Consistency-driven Rationale Evaluation for Self-Training), a self-training framework that further evaluates each rationale through follow-up questions.
• Score: 15.124701883286436
• License:
• Abstract: Self-training approach for large language models (LLMs) improves reasoning abilities by training the models on their self-generated rationales. Previous approaches have labeled rationales that produce correct answers for a given question as appropriate for training. However, a single measure risks misjudging rationale quality, leading the models to learn flawed reasoning patterns. To address this issue, we propose CREST (Consistency-driven Rationale Evaluation for Self-Training), a self-training framework that further evaluates each rationale through follow-up questions and leverages this evaluation to guide its training. Specifically, we introduce two methods: (1) filtering out rationales that frequently result in incorrect answers on follow-up questions and (2) preference learning based on mixed preferences from rationale evaluation results of both original and follow-up questions. Experiments on three question-answering datasets using open LLMs show that CREST not only improves the logical robustness and correctness of rationales but also improves reasoning abilities compared to previous self-training approaches.

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