  Warning: This paper contains content that may be offensive or upsetting.
There has been a significant increase in the usage of large language models
(LLMs) in various applications, both in their original form and through
fine-tuned adaptations. As a result, LLMs have gained popularity and are being
widely adopted by a large user community. However, one of the concerns with
LLMs is the potential generation of socially biased content. The existing
evaluation methods have many constraints, and their results exhibit a limited
degree of interpretability. In this work, we propose a bias evaluation
framework named GPTBIAS that leverages the high performance of LLMs (e.g.,
GPT-4 \cite{openai2023gpt4}) to assess bias in models. We also introduce
prompts called Bias Attack Instructions, which are specifically designed for
evaluating model bias. To enhance the credibility and interpretability of bias
evaluation, our framework not only provides a bias score but also offers
detailed information, including bias types, affected demographics, keywords,
reasons behind the biases, and suggestions for improvement. We conduct
extensive experiments to demonstrate the effectiveness and usability of our
bias evaluation framework.
