ReviewInstruct: A Review-Driven Multi-Turn Conversations Generation Method for Large Language Models

Jiangxu Wu; Cong Wang; TianHuang Su; Jun Yang; Haozhi Lin; Chao Zhang; Ming Peng; Kai Shi; SongPan Yang; BinQing Pan; ZiXian Li; Ni Yang; ZhenYu Yang

arXiv · 2505.11010

ReviewInstruct: A Review-Driven Multi-Turn Conversations Generation Method for Large Language Models

Jiangxu Wu·Cong Wang·TianHuang Su·Jun Yang·Haozhi Lin·Chao Zhang·Ming Peng·Kai Shi·SongPan Yang·BinQing Pan·ZiXian Li·Ni Yang·ZhenYu Yang

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Abstract

The effectiveness of large language models (LLMs) in conversational AI is hindered by their reliance on single-turn supervised fine-tuning (SFT) data, which limits contextual coherence in multi-turn dialogues. Existing methods for generating multi-turn dialogue data struggle to ensure both diversity and quality in instructions. To address this, we propose Review-Instruct, a novel framework that synthesizes multi-turn conversations through an iterative "Ask-Respond-Review" process involving three agent roles: a Candidate, multiple Reviewers, and a Chairman. The framework iteratively refines instructions by incorporating Reviewer feedback, enhancing dialogue diversity and difficulty. We construct a multi-turn dataset using the Alpaca dataset and fine-tune the LLaMA2-13B model. Evaluations on MT-Bench, MMLU-Pro, and Auto-Arena demonstrate significant improvements, achieving absolute gains of 2.9\% on MMLU-Pro and 2\% on MT-Bench compared to prior state-of-the-art models based on LLaMA2-13B. Ablation studies confirm the critical role of the Review stage and the use of multiple Reviewers in boosting instruction diversity and difficulty. Our work highlights the potential of review-driven, multi-agent frameworks for generating high-quality conversational data at scale.

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BibTeX RIS

Jiangxu Wu, Cong Wang, TianHuang Su, Jun Yang, Haozhi Lin, Chao Zhang, Ming Peng, Kai Shi, SongPan Yang, BinQing Pan, ZiXian Li, Ni Yang, ZhenYu Yang. 2025-07-04. ReviewInstruct: A Review-Driven Multi-Turn Conversations Generation Method for Large Language Models. https://arxiv.org/abs/2505.11010

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