@inproceedings{wu-etal-2026-coig,
title = "{COIG}-{P}: A High-Quality and Large-Scale {C}hinese Preference Dataset for Alignment with Human Values",
author = "Wu, Siwei and
Ren, JinCheng and
Du, Xeron and
Guo, Shuyue and
Qu, Xingwei and
Liang, Yiming and
Liu, Jie and
Li, Yunwen and
Loakman, Tyler and
Zheng, Tianyu and
Feng, Boyu and
Yuan, Huaqing and
Wang, Zili and
Liu, Jiaheng and
Huang, Wenhao and
Cai, Chenglin and
Que, Haoran and
Yang, Jian and
Bai, Yuelin and
Wang, Zekun Moore and
Yu, Zhouliang and
Lin, Qunshu and
Pan, Ding and
Jiang, Yuchen Eleanor and
Wang, Tiannan and
Zhou, Wangchunshu and
Wang, Shenzhi and
Bu, Xingyuan and
Liu, Minghao and
Wang, Guoyin and
Zhang, Ge and
Lin, Chenghua",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-eacl.288/",
pages = "5420--5447",
ISBN = "979-8-89176-386-9",
abstract = "Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation. Human annotation significantly limits the scalability of human preference datasets. As a result, Chinese Alignment and Chinese Reward Models (CRM) have not yet been thoroughly explored. To address these challenges, we design an LLM-based data annotation pipeline with no human intervention. Based on this pipeline, we curate COIG-P (Chinese Open Instruction Generalist - Preference), a high-quality, large-scale Chinese preference dataset consisting of 1M Chinese preference pairs and 92k carefully curated Chinese queries across diverse domains, including Chat, Coding, Maths, and others. We conduct experiments to verify the quality of COIG-P from two perspectives. (1) COIG-P brings significant performance improvements for the Qwen2/2.5 and Infinity-Instruct model series on AlignBench through DPO, with gains ranging from 2{\%} to 12{\%}. Furthermore, it significantly outperforms other existing Chinese preference datasets. (2) We train an 8B-sized CRM and manually annotate a Chinese Reward Benchmark (CRBench). Our CRM demonstrates robust scoring ability on CRBench. In addition, in practical data construction experiments, the quality of the data constructed by our CRM is comparable to that produced by GPT-4o."
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<abstract>Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation. Human annotation significantly limits the scalability of human preference datasets. As a result, Chinese Alignment and Chinese Reward Models (CRM) have not yet been thoroughly explored. To address these challenges, we design an LLM-based data annotation pipeline with no human intervention. Based on this pipeline, we curate COIG-P (Chinese Open Instruction Generalist - Preference), a high-quality, large-scale Chinese preference dataset consisting of 1M Chinese preference pairs and 92k carefully curated Chinese queries across diverse domains, including Chat, Coding, Maths, and others. We conduct experiments to verify the quality of COIG-P from two perspectives. (1) COIG-P brings significant performance improvements for the Qwen2/2.5 and Infinity-Instruct model series on AlignBench through DPO, with gains ranging from 2% to 12%. Furthermore, it significantly outperforms other existing Chinese preference datasets. (2) We train an 8B-sized CRM and manually annotate a Chinese Reward Benchmark (CRBench). Our CRM demonstrates robust scoring ability on CRBench. In addition, in practical data construction experiments, the quality of the data constructed by our CRM is comparable to that produced by GPT-4o.</abstract>
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%0 Conference Proceedings
%T COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values
%A Wu, Siwei
%A Ren, JinCheng
%A Du, Xeron
%A Guo, Shuyue
%A Qu, Xingwei
%A Liang, Yiming
%A Liu, Jie
%A Li, Yunwen
%A Loakman, Tyler
%A Zheng, Tianyu
%A Feng, Boyu
%A Yuan, Huaqing
%A Wang, Zili
%A Liu, Jiaheng
%A Huang, Wenhao
%A Cai, Chenglin
%A Que, Haoran
%A Yang, Jian
%A Bai, Yuelin
%A Wang, Zekun Moore
%A Yu, Zhouliang
%A Lin, Qunshu
%A Pan, Ding
%A Jiang, Yuchen Eleanor
%A Wang, Tiannan
%A Zhou, Wangchunshu
%A Wang, Shenzhi
%A Bu, Xingyuan
%A Liu, Minghao
%A Wang, Guoyin
%A Zhang, Ge
%A Lin, Chenghua
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F wu-etal-2026-coig
%X Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation. Human annotation significantly limits the scalability of human preference datasets. As a result, Chinese Alignment and Chinese Reward Models (CRM) have not yet been thoroughly explored. To address these challenges, we design an LLM-based data annotation pipeline with no human intervention. Based on this pipeline, we curate COIG-P (Chinese Open Instruction Generalist - Preference), a high-quality, large-scale Chinese preference dataset consisting of 1M Chinese preference pairs and 92k carefully curated Chinese queries across diverse domains, including Chat, Coding, Maths, and others. We conduct experiments to verify the quality of COIG-P from two perspectives. (1) COIG-P brings significant performance improvements for the Qwen2/2.5 and Infinity-Instruct model series on AlignBench through DPO, with gains ranging from 2% to 12%. Furthermore, it significantly outperforms other existing Chinese preference datasets. (2) We train an 8B-sized CRM and manually annotate a Chinese Reward Benchmark (CRBench). Our CRM demonstrates robust scoring ability on CRBench. In addition, in practical data construction experiments, the quality of the data constructed by our CRM is comparable to that produced by GPT-4o.
%U https://aclanthology.org/2026.findings-eacl.288/
%P 5420-5447
Markdown (Informal)
[COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values](https://aclanthology.org/2026.findings-eacl.288/) (Wu et al., Findings 2026)
ACL
- Siwei Wu, JinCheng Ren, Xeron Du, Shuyue Guo, Xingwei Qu, Yiming Liang, Jie Liu, Yunwen Li, Tyler Loakman, Tianyu Zheng, Boyu Feng, Huaqing Yuan, Zili Wang, Jiaheng Liu, Wenhao Huang, Chenglin Cai, Haoran Que, Jian Yang, Yuelin Bai, Zekun Moore Wang, Zhouliang Yu, Qunshu Lin, Ding Pan, Yuchen Eleanor Jiang, Tiannan Wang, Wangchunshu Zhou, Shenzhi Wang, Xingyuan Bu, Minghao Liu, Guoyin Wang, Ge Zhang, and Chenghua Lin. 2026. COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values. In Findings of the Association for Computational Linguistics: EACL 2026, pages 5420–5447, Rabat, Morocco. Association for Computational Linguistics.