CBBQ: A Chinese Bias Benchmark Dataset Curated with Human-AI
  Collaboration for Large Language Models
Yufei Huang Deyi Xiong
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 via extensive literature review, ambiguous context
generation, AI-assisted disambiguous context generation, snd manual review \&
recomposition. The testing instances in the dataset are automatically derived
from 3K+ high-quality templates manually authored with stringent quality
control. The dataset exhibits wide coverage and high diversity. Extensive
experiments demonstrate the effectiveness of the dataset in detecting model
bias, with all 10 publicly available Chinese large language models exhibiting
strong bias in certain categories. Additionally, we observe from our
experiments that fine-tuned models could, to a certain extent, heed
instructions and avoid generating outputs that are morally harmful in some
types, in the way of "moral self-correction". Our dataset and results are
publicly available at
\href{https://github.com/YFHuangxxxx/CBBQ}{https://github.com/YFHuangxxxx/CBBQ},
offering debiasing research opportunities to a widened community.