@inproceedings{zhang-etal-2025-iheval,
    title = "{IHE}val: Evaluating Language Models on Following the Instruction Hierarchy",
    author = "Zhang, Zhihan  and
      Li, Shiyang  and
      Zhang, Zixuan  and
      Liu, Xin  and
      Jiang, Haoming  and
      Tang, Xianfeng  and
      Gao, Yifan  and
      Li, Zheng  and
      Wang, Haodong  and
      Tan, Zhaoxuan  and
      Li, Yichuan  and
      Yin, Qingyu  and
      Yin, Bing  and
      Jiang, Meng",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.naacl-long.425/",
    pages = "8374--8398",
    ISBN = "979-8-89176-189-6",
    abstract = "The instruction hierarchy, which establishes a priority order from system messages to user messages, conversation history, and tool outputs, is essential for ensuring consistent and safe behavior in language models (LMs). Despite its importance, this topic receives limited attention, and there is a lack of comprehensive benchmarks for evaluating models' ability to follow the instruction hierarchy. We bridge this gap by introducing IHEval, a novel benchmark comprising 3,538 examples across nine tasks, covering cases where instructions in different priorities either align or conflict. Our evaluation of popular LMs highlights their struggle to recognize instruction priorities. All evaluated models experience a sharp performance decline when facing conflicting instructions, compared to their original instruction-following performance. Moreover, the most competitive open-source model only achieves 48{\%} accuracy in resolving such conflicts. Our results underscore the need for targeted optimization in the future development of LMs."
}