@inproceedings{wu-etal-2025-review,
title = "Review-Instruct: A Review-Driven Multi-Turn Conversations Generation Method for Large Language Models",
author = "Wu, Jiangxu and
Wang, Cong and
Su, TianHuang and
Yang, Jun and
Lin, Haozhi and
Zhang, Chao and
Peng, Ming and
Shi, Kai and
Yang, SongPan and
Pan, BinQiang and
Li, ZiXian",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.851/",
doi = "10.18653/v1/2025.findings-acl.851",
pages = "16578--16595",
ISBN = "979-8-89176-256-5",
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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<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.</abstract>
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%0 Conference Proceedings
%T Review-Instruct: A Review-Driven Multi-Turn Conversations Generation Method for Large Language Models
%A Wu, Jiangxu
%A Wang, Cong
%A Su, TianHuang
%A Yang, Jun
%A Lin, Haozhi
%A Zhang, Chao
%A Peng, Ming
%A Shi, Kai
%A Yang, SongPan
%A Pan, BinQiang
%A Li, ZiXian
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F wu-etal-2025-review
%X 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.
%R 10.18653/v1/2025.findings-acl.851
%U https://aclanthology.org/2025.findings-acl.851/
%U https://doi.org/10.18653/v1/2025.findings-acl.851
%P 16578-16595
Markdown (Informal)
[Review-Instruct: A Review-Driven Multi-Turn Conversations Generation Method for Large Language Models](https://aclanthology.org/2025.findings-acl.851/) (Wu et al., Findings 2025)
ACL
- Jiangxu Wu, Cong Wang, TianHuang Su, Jun Yang, Haozhi Lin, Chao Zhang, Ming Peng, Kai Shi, SongPan Yang, BinQiang Pan, and ZiXian Li. 2025. Review-Instruct: A Review-Driven Multi-Turn Conversations Generation Method for Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2025, pages 16578–16595, Vienna, Austria. Association for Computational Linguistics.