@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T New Terms, New Toxicity: Consensus-based Chinese Neologism Toxicity Detection via Search-Augmented LLMs
%A Cui, Shiyao
%A Zhang, QingLin
%A Wang, Di
%A Lu, Yida
%A Zhang, Zhexin
%A Gao, Jinhua
%A Yang, Jinglin
%A He, Min
%A Qiu, Han
%A Huang, Minlie
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F cui-etal-2026-new
%X 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.
%U https://aclanthology.org/2026.acl-long.1602/
%P 34683-34701
Markdown (Informal)
[New Terms, New Toxicity: Consensus-based Chinese Neologism Toxicity Detection via Search-Augmented LLMs](https://aclanthology.org/2026.acl-long.1602/) (Cui et al., ACL 2026)
ACL
- Shiyao Cui, QingLin Zhang, Di Wang, Yida Lu, Zhexin Zhang, Jinhua Gao, Jinglin Yang, Min He, Han Qiu, and Minlie Huang. 2026. New Terms, New Toxicity: Consensus-based Chinese Neologism Toxicity Detection via Search-Augmented LLMs. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 34683–34701, San Diego, California, United States. Association for Computational Linguistics.