@inproceedings{liu-etal-2026-llms-hear,
    title = "Can {LLM}s Hear the Dogwhistle?",
    author = "Liu, Yifan  and
      Lin, Yi  and
      Guo, Xinwei  and
      Wang, Ziwei  and
      Zhang, Jiaxin  and
      Chen, Guanhua  and
      Wu, Haiyan  and
      Zhao, Xiangyu  and
      Yao, Xin  and
      Wei, Xuetao",
    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.161/",
    pages = "3256--3273",
    ISBN = "979-8-89176-395-1",
    abstract = "With the widespread deployment of large language models (LLMs), existing safety benchmarks remain largely focused on explicitly harmful content, overlooking context-dependent expressions such as dogwhistles, the language that conveys harmful intent while appearing benign on the surface. To address this gap, we introduce DogBench, a comprehensive benchmark for evaluating LLM safety under dogwhistle-driven prompts. DogBench comprises 11,150 prompt instances constructed from controlled templates that embed dogwhistle terms, allowing for enabling direct comparison with explicit toxic terms under identical prompt structures. Each prompt is further annotated with pragmatic attributes, including interaction category and stance tendency. Extensive evaluations across multiple mainstream LLMs reveal a consistent pattern: dogwhistle prompts are substantially more likely to elicit harmful outputs than their explicit toxic counterparts, with an average risk increase of approximately fourfold. These findings expose a blind spot in current safety evaluation and alignment practices. Our work underscores the need to explicitly incorporate dogwhistles into future LLM safety research, with DogBench serving as a dedicated benchmark for this purpose."
}