ChineseHarm-Bench: A Chinese Harmful Content Detection Benchmark
Large language models (LLMs) have been increasingly applied to automated
harmful content detection tasks, assisting moderators in identifying policy
violations and improving the overall efficiency and accuracy of content review.
However, existing resources for harmful content detection are predominantly
focused on English, with Chinese datasets remaining scarce and often limited in
scope. We present a comprehensive, professionally annotated benchmark for
Chinese content harm detection, which covers six representative categories and
is constructed entirely from real-world data. Our annotation process further
yields a knowledge rule base that provides explicit expert knowledge to assist
LLMs in Chinese harmful content detection. In addition, we propose a
knowledge-augmented baseline that integrates both human-annotated knowledge
rules and implicit knowledge from large language models, enabling smaller
models to achieve performance comparable to state-of-the-art LLMs. Code and
data are available at https://github.com/zjunlp/ChineseHarm-bench.
684bb33a3b733ba3336870c5
Kangwei Liu
684bb33a3b733ba3336870c6
Siyuan Cheng
684bb33a3b733ba3336870c7
Bozhong Tian
684bb33a3b733ba3336870c8
Xiaozhuan Liang
684bb33a3b733ba3336870c9
Yuyang Yin
684bb33a3b733ba3336870ca
Meng Han
684bb33a3b733ba3336870cb
Ningyu Zhang
claimed_verified
2025-06-13T07:38:50.549Z
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/avatars/e0fccbb2577d76088e09f054c35cffbc.svg
Ningyu Zhang
user
Ningyu
684bb33a3b733ba3336870cc
Bryan Hooi
684bb33a3b733ba3336870cd
Xi Chen
extracted_pending
2025-06-13T05:12:27.526Z
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/avatars/f80df906b722b4901debce9baa867073.svg
chen
user
Jasonchen123
684bb33a3b733ba3336870ce
Shumin Deng
ChineseHarm-Bench: A Chinese Harmful Content Detection Benchmark
Large language models (LLMs) have been increasingly applied to automated
harmful content detection tasks, assisting moderators in identifying policy
violations and improving the overall efficiency and accuracy of content review.
However, existing resources for harmful content detection are predominantly
focused on English, with Chinese datasets remaining scarce and often limited in
scope. We present a comprehensive, professionally annotated benchmark for
Chinese content harm detection, which covers six representative categories and
is constructed entirely from real-world data. Our annotation process further
yields a knowledge rule base that provides explicit expert knowledge to assist
LLMs in Chinese harmful content detection. In addition, we propose a
knowledge-augmented baseline that integrates both human-annotated knowledge
rules and implicit knowledge from large language models, enabling smaller
models to achieve performance comparable to state-of-the-art LLMs. Code and
data are available at https://github.com/zjunlp/ChineseHarm-bench.
A benchmark for Chinese harmful content detection is introduced, along with a knowledge-augmented model that enhances efficiency and accuracy using human-annotated rules and LLMs.
large language models LLMs harmful content detection knowledge-augmented baseline annotation process knowledge rule base Chinese datasets