@inproceedings{xu-etal-2024-course,
title = "Course-Correction: Safety Alignment Using Synthetic Preferences",
author = "Xu, Rongwu and
Cai, Yishuo and
Zhou, Zhenhong and
Gu, Renjie and
Weng, Haiqin and
Yan, Liu and
Zhang, Tianwei and
Xu, Wei and
Qiu, Han",
editor = "Dernoncourt, Franck and
Preo{\c{t}}iuc-Pietro, Daniel and
Shimorina, Anastasia",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2024",
address = "Miami, Florida, US",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-industry.119",
pages = "1622--1649",
abstract = "The risk of harmful contents generated by large language models (LLMs) becomes a critical concern. This paper systematically evaluates and enhances LLMs{'} capability to perform \textit{course-correction}, , the model can steer away from generating harmful content autonomously. First, we introduce the C$^2$-Eval benchmark for quantitative assessment and analyze 10 popular LLMs, revealing varying proficiency of current safety-tuned LLMs in course-correction.To improve, we propose fine-tuning LLMs with preference learning, emphasizing the preference for timely course-correction. Using an automated pipeline, we create C$^2$-Syn, a synthetic C$^2$-Syn with 750K pairwise preferences, to teach models the concept of timely course-correction through data-driven learning.Experiments on Llama2-Chat 7B and Qwen2 7B show that our method effectively enhances course-correction skills without affecting general performance. Additionally, it effectively improves LLMs{'} safety, particularly in resisting jailbreak attacks.",
}
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<abstract>The risk of harmful contents generated by large language models (LLMs) becomes a critical concern. This paper systematically evaluates and enhances LLMs’ capability to perform course-correction, , the model can steer away from generating harmful content autonomously. First, we introduce the C²-Eval benchmark for quantitative assessment and analyze 10 popular LLMs, revealing varying proficiency of current safety-tuned LLMs in course-correction.To improve, we propose fine-tuning LLMs with preference learning, emphasizing the preference for timely course-correction. Using an automated pipeline, we create C²-Syn, a synthetic C²-Syn with 750K pairwise preferences, to teach models the concept of timely course-correction through data-driven learning.Experiments on Llama2-Chat 7B and Qwen2 7B show that our method effectively enhances course-correction skills without affecting general performance. Additionally, it effectively improves LLMs’ safety, particularly in resisting jailbreak attacks.</abstract>
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%0 Conference Proceedings
%T Course-Correction: Safety Alignment Using Synthetic Preferences
%A Xu, Rongwu
%A Cai, Yishuo
%A Zhou, Zhenhong
%A Gu, Renjie
%A Weng, Haiqin
%A Yan, Liu
%A Zhang, Tianwei
%A Xu, Wei
%A Qiu, Han
%Y Dernoncourt, Franck
%Y Preoţiuc-Pietro, Daniel
%Y Shimorina, Anastasia
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, US
%F xu-etal-2024-course
%X The risk of harmful contents generated by large language models (LLMs) becomes a critical concern. This paper systematically evaluates and enhances LLMs’ capability to perform course-correction, , the model can steer away from generating harmful content autonomously. First, we introduce the C²-Eval benchmark for quantitative assessment and analyze 10 popular LLMs, revealing varying proficiency of current safety-tuned LLMs in course-correction.To improve, we propose fine-tuning LLMs with preference learning, emphasizing the preference for timely course-correction. Using an automated pipeline, we create C²-Syn, a synthetic C²-Syn with 750K pairwise preferences, to teach models the concept of timely course-correction through data-driven learning.Experiments on Llama2-Chat 7B and Qwen2 7B show that our method effectively enhances course-correction skills without affecting general performance. Additionally, it effectively improves LLMs’ safety, particularly in resisting jailbreak attacks.
%U https://aclanthology.org/2024.emnlp-industry.119
%P 1622-1649
Markdown (Informal)
[Course-Correction: Safety Alignment Using Synthetic Preferences](https://aclanthology.org/2024.emnlp-industry.119) (Xu et al., EMNLP 2024)
ACL
- Rongwu Xu, Yishuo Cai, Zhenhong Zhou, Renjie Gu, Haiqin Weng, Liu Yan, Tianwei Zhang, Wei Xu, and Han Qiu. 2024. Course-Correction: Safety Alignment Using Synthetic Preferences. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1622–1649, Miami, Florida, US. Association for Computational Linguistics.