{
 "id": "walledai/CBBQ",
 "owner": "walledai",
 "name": "CBBQ",
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 "last_modified": "2024-10-18T17:38:25.000Z",
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 "citation": "@inproceedings{huang-xiong-2024-cbbq,\n    title = \"{CBBQ}: A {C}hinese Bias Benchmark Dataset Curated with Human-{AI} Collaboration for Large Language Models\",\n    author = \"Huang, Yufei  and\n      Xiong, Deyi\",\n    editor = \"Calzolari, Nicoletta  and\n      Kan, Min-Yen  and\n      Hoste, Veronique  and\n      Lenci, Alessandro  and\n      Sakti, Sakriani  and\n      Xue, Nianwen\",\n    booktitle = \"Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)\",\n    month = may,\n    year = \"2024\",\n    address = \"Torino, Italia\",\n    publisher = \"ELRA and ICCL\",\n    url = \"https://aclanthology.org/2024.lrec-main.260\",\n    pages = \"2917--2929\",\n    abstract = \"Holistically measuring societal biases of large language models is crucial for detecting and reducing ethical risks in highly capable AI models. In this work, we present a Chinese Bias Benchmark dataset that consists of over 100K questions jointly constructed by human experts and generative language models, covering stereotypes and societal biases in 14 social dimensions related to Chinese culture and values. The curation process contains 4 essential steps: bias identification, ambiguous context generation, AI-assisted disambiguous context generation, and manual review and recomposition. The testing instances in the dataset are automatically derived from 3K+ high-quality templates manually authored with stringent quality control. The dataset exhibits w",
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