@inproceedings{zeng-etal-2025-order,
title = "Order Matters: Investigate the Position Bias in Multi-constraint Instruction Following",
author = "Zeng, Jie and
He, Qianyu and
Ren, Qingyu and
Liang, Jiaqing and
Zhou, Weikang and
Sun, Zeye and
Yu, Fei and
Xiao, Yanghua",
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.646/",
doi = "10.18653/v1/2025.findings-acl.646",
pages = "12479--12492",
ISBN = "979-8-89176-256-5",
abstract = "Real-world instructions with multiple constraints pose a significant challenge to existing large language models (LLMs). An observation is that the LLMs exhibit dramatic performance fluctuation when disturbing the order of the incorporated constraints. Yet, none of the existing works has systematically investigated this position bias problem in the field of multi-constraint instruction following. To bridge this gap, we design a probing task where we quantitatively measure the difficulty distribution of the constraints by a novel Difficulty Distribution Index (CDDI). Through the experimental results, we find that LLMs are more performant when presented with the constraints in a ``hard-to-easy'' order. This preference can be generalized to LLMs with different architecture or different sizes of parameters. Additionally, we conduct an explanation study, providing an intuitive insight into the correlation between the LLM{'}s attention and constraint orders. Our code and dataset are publicly available at https://github.com/meowpass/PBIF."
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<abstract>Real-world instructions with multiple constraints pose a significant challenge to existing large language models (LLMs). An observation is that the LLMs exhibit dramatic performance fluctuation when disturbing the order of the incorporated constraints. Yet, none of the existing works has systematically investigated this position bias problem in the field of multi-constraint instruction following. To bridge this gap, we design a probing task where we quantitatively measure the difficulty distribution of the constraints by a novel Difficulty Distribution Index (CDDI). Through the experimental results, we find that LLMs are more performant when presented with the constraints in a “hard-to-easy” order. This preference can be generalized to LLMs with different architecture or different sizes of parameters. Additionally, we conduct an explanation study, providing an intuitive insight into the correlation between the LLM’s attention and constraint orders. Our code and dataset are publicly available at https://github.com/meowpass/PBIF.</abstract>
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%0 Conference Proceedings
%T Order Matters: Investigate the Position Bias in Multi-constraint Instruction Following
%A Zeng, Jie
%A He, Qianyu
%A Ren, Qingyu
%A Liang, Jiaqing
%A Zhou, Weikang
%A Sun, Zeye
%A Yu, Fei
%A Xiao, Yanghua
%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 zeng-etal-2025-order
%X Real-world instructions with multiple constraints pose a significant challenge to existing large language models (LLMs). An observation is that the LLMs exhibit dramatic performance fluctuation when disturbing the order of the incorporated constraints. Yet, none of the existing works has systematically investigated this position bias problem in the field of multi-constraint instruction following. To bridge this gap, we design a probing task where we quantitatively measure the difficulty distribution of the constraints by a novel Difficulty Distribution Index (CDDI). Through the experimental results, we find that LLMs are more performant when presented with the constraints in a “hard-to-easy” order. This preference can be generalized to LLMs with different architecture or different sizes of parameters. Additionally, we conduct an explanation study, providing an intuitive insight into the correlation between the LLM’s attention and constraint orders. Our code and dataset are publicly available at https://github.com/meowpass/PBIF.
%R 10.18653/v1/2025.findings-acl.646
%U https://aclanthology.org/2025.findings-acl.646/
%U https://doi.org/10.18653/v1/2025.findings-acl.646
%P 12479-12492
Markdown (Informal)
[Order Matters: Investigate the Position Bias in Multi-constraint Instruction Following](https://aclanthology.org/2025.findings-acl.646/) (Zeng et al., Findings 2025)
ACL
- Jie Zeng, Qianyu He, Qingyu Ren, Jiaqing Liang, Weikang Zhou, Zeye Sun, Fei Yu, and Yanghua Xiao. 2025. Order Matters: Investigate the Position Bias in Multi-constraint Instruction Following. In Findings of the Association for Computational Linguistics: ACL 2025, pages 12479–12492, Vienna, Austria. Association for Computational Linguistics.