@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://preview.aclanthology.org/acl25-workshop-ingestion/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."
}