Live search and generation settings can introduce small drift during
reproduction.
1python scripts/run_inference.py \
2 --model_path thu-coai/SeTox-Qwen2.5-7B \
3 --search_backend serper \
4 --input_path data/eval/neologism_test.json \
5 --output_path outputs/neologism_predictions.json
This model is intended for research on Chinese neologism toxicity detection. It
can make mistakes on ambiguous, fast-changing, adversarial, or under-specified
terms. Search results may contain noisy or unsafe snippets, and downstream users
should apply appropriate safety review before deployment.
1@inproceedings{cui-etal-2026-setox,
2 title = "{S}e{T}ox: Search-enhanced Reasoning for {LLM}-based Toxicity Detection over {C}hinese Internet Buzzwords",
3 author = "Cui, Yiming and Zhang, Qinglin and Su, Xu and Min, Changyu and Hu, Shilin and Huang, Minlie",
4 editor = "Che, Wanxiang and Nabende, Joyce and Shutova, Ekaterina and Pilehvar, Mohammad Taher",
5 booktitle = "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
6 month = jul,
7 year = "2026",
8 address = "San Diego, California, USA",
9 publisher = "Association for Computational Linguistics",
10 url = "https://aclanthology.org/2026.acl-long.1602/",
11 doi = "10.18653/v1/2026.acl-long.1602",
12 pages = "33018--33034"
13}