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

TL;DR

CREST enhances large language models' reasoning by evaluating and filtering rationales through follow-up questions, leading to more robust and correct reasoning capabilities.

Contribution

It introduces a novel framework that evaluates rationales via follow-up questions and uses this to improve self-training of LLMs.

Findings

Improves logical robustness of rationales

Enhances reasoning accuracy over previous methods

Effective across multiple question-answering datasets

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.