@inproceedings{cui-etal-2026-new,
    title = "New Terms, New Toxicity: Consensus-based {C}hinese Neologism Toxicity Detection via Search-Augmented {LLM}s",
    author = "Cui, Shiyao  and
      Zhang, QingLin  and
      Wang, Di  and
      Lu, Yida  and
      Zhang, Zhexin  and
      Gao, Jinhua  and
      Yang, Jinglin  and
      He, Min  and
      Qiu, Han  and
      Huang, Minlie",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1602/",
    pages = "34683--34701",
    ISBN = "979-8-89176-390-6",
    abstract = "Neologisms, emerging terms in meaning or form, can serve as new vehicles for toxic expression, like ``田园女'' ({''}country girl'') as a stigmatizing label targeting feminism. Such toxic neologisms appear benign but have evolved into toxic usage in public consensus, posing challenges to moderation systems and remaining underexplored. In this paper, we investigate how to detect implicit toxicity expressed via neologisms. We first propose a taxonomy that captures the origins and consensus-verification criteria of toxic neologisms, followed by the construction of a lexicon spanning widely observed risk categories. To capture toxicity grounded in public consensus, we introduce **SeTox**, a search-augmented framework that enables static large language models (LLMs) to incorporate real-time web context for neologism toxicity detection. Experiments show that **SeTox**, even with 3B-scale models, outperforms recent large-scale models, demonstrating its scalability to incorporate real-world knowledge for toxic neologism detection. **Disclaimer**: this paper has offensive contents that may be disturbing to some readers."
}