GPTBIAS: A Comprehensive Framework for Evaluating Bias in Large Language Models

Jiaxu Zhao, Meng Fang, Shirui Pan, Wenpeng Yin, Mykola Pechenizkiy

Keywords: Large Language Model, Bias

Abstract: Large language models (LLMs) have seen widespread adoption across various applications, both in their original form and fine-tuned adaptations. However, a major concern with LLMs is their potential to generate biased content. Existing evaluation methods often have different constraints, such as needing access to the model's intermediate outputs. To address these issues, we propose GPTBIAS, a novel bias evaluation framework that leverages the capabilities of advanced LLMs like GPT-4 to assess bias in other models across nine bias types. Our framework introduces Bias Attack Instructions, specifically designed to evaluate model bias across multiple dimensions. GPTBIAS provides not only a quantitative bias score but also detailed information on bias types, affected demographics, underlying reasons for biases, and suggestions for improvement. Through extensive experiments on popular LLMs, we demonstrate the effectiveness and usability of our bias evaluation framework. Our results reveal nuanced insights into the biases present in different models and highlight the importance of comprehensive bias assessment in the development and deployment of LLMs.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.

Reciprocal Reviewing: I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Submission Number: 14068