@inproceedings{bao-etal-2026-policyllm,
    title = "{P}olicy{LLM}: Towards Excellent Comprehension of Public Policy for Large Language Models",
    author = "Bao, Han  and
      Zhang, Penghao  and
      Huang, Yue  and
      Yuan, Zhengqing  and
      Ru, Yanchi  and
      Rui, SU  and
      Zhou, Yujun  and
      Wang, Xiangqi  and
      Guo, Kehan  and
      Chawla, Nitesh V  and
      Ye, Yanfang  and
      Zhang, Xiangliang",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.107/",
    pages = "2249--2274",
    ISBN = "979-8-89176-395-1",
    abstract = "Large Language Models (LLMs) are increasingly integrated into real-world decision-making, including in the domain of public policy. Yet, their ability to comprehend and reason about policy-related content remains underexplored. To fill this gap, we present PolicyBench, the first large-scale bilingual benchmark evaluating policy comprehension, comprising 21K cases across a broad spectrum of policy areas, capturing the diversity and complexity of real-world governance. Following Bloom{'}s taxonomy, the benchmark assesses three core capabilities: (1) Memorization: factual recall of policy knowledge, (2) Understanding: conceptual and contextual reasoning, and (3) Application: problem-solving in real-life policy scenarios. Building on this benchmark, we further propose PolicyMoE, a domain-specialized Mixture-of-Experts (MoE) model with expert modules aligned to each cognitive level. The proposed models demonstrate stronger performance on application-oriented policy tasks than on memorization or conceptual understanding, and yields the highest accuracy on structured reasoning tasks. Our results reveal key limitations of current LLMs in policy understanding and suggest paths toward more reliable, policy-focused models"
}