@inproceedings{he-etal-2024-complex,
title = "From Complex to Simple: Enhancing Multi-Constraint Complex Instruction Following Ability of Large Language Models",
author = "He, Qianyu and
Zeng, Jie and
He, Qianxi and
Liang, Jiaqing and
Xiao, Yanghua",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.637",
pages = "10864--10882",
abstract = "It is imperative for Large language models (LLMs) to follow instructions with elaborate requirements (i.e. Complex Instructions Following). Yet, it remains under-explored how to enhance the ability of LLMs to follow complex instructions with multiple constraints. To bridge the gap, we initially study what training data is effective in enhancing complex constraints following abilities. We found that training LLMs with instructions containing multiple constraints enhances their understanding of complex instructions, especially those with lower complexity levels. Additionally, we further propose methods addressing how to obtain and utilize the effective training data. Finally, we conduct extensive experiments to prove the effectiveness of our methods in terms of overall performance and training efficiency. We also demonstrate that our methods improve models{'} ability to follow instructions generally and generalize effectively across out-of-domain, in domain, and adversarial settings, while maintaining general capabilities.",
}
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<abstract>It is imperative for Large language models (LLMs) to follow instructions with elaborate requirements (i.e. Complex Instructions Following). Yet, it remains under-explored how to enhance the ability of LLMs to follow complex instructions with multiple constraints. To bridge the gap, we initially study what training data is effective in enhancing complex constraints following abilities. We found that training LLMs with instructions containing multiple constraints enhances their understanding of complex instructions, especially those with lower complexity levels. Additionally, we further propose methods addressing how to obtain and utilize the effective training data. Finally, we conduct extensive experiments to prove the effectiveness of our methods in terms of overall performance and training efficiency. We also demonstrate that our methods improve models’ ability to follow instructions generally and generalize effectively across out-of-domain, in domain, and adversarial settings, while maintaining general capabilities.</abstract>
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%0 Conference Proceedings
%T From Complex to Simple: Enhancing Multi-Constraint Complex Instruction Following Ability of Large Language Models
%A He, Qianyu
%A Zeng, Jie
%A He, Qianxi
%A Liang, Jiaqing
%A Xiao, Yanghua
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F he-etal-2024-complex
%X It is imperative for Large language models (LLMs) to follow instructions with elaborate requirements (i.e. Complex Instructions Following). Yet, it remains under-explored how to enhance the ability of LLMs to follow complex instructions with multiple constraints. To bridge the gap, we initially study what training data is effective in enhancing complex constraints following abilities. We found that training LLMs with instructions containing multiple constraints enhances their understanding of complex instructions, especially those with lower complexity levels. Additionally, we further propose methods addressing how to obtain and utilize the effective training data. Finally, we conduct extensive experiments to prove the effectiveness of our methods in terms of overall performance and training efficiency. We also demonstrate that our methods improve models’ ability to follow instructions generally and generalize effectively across out-of-domain, in domain, and adversarial settings, while maintaining general capabilities.
%U https://aclanthology.org/2024.findings-emnlp.637
%P 10864-10882
Markdown (Informal)
[From Complex to Simple: Enhancing Multi-Constraint Complex Instruction Following Ability of Large Language Models](https://aclanthology.org/2024.findings-emnlp.637) (He et al., Findings 2024)
ACL