Discovering Bias Associations through Open-Ended LLM Generations

• URL: http://arxiv.org/abs/2508.01412v1
• Date: Sat, 02 Aug 2025 15:31:55 GMT
• Title: Discovering Bias Associations through Open-Ended LLM Generations
• Authors: Jinhao Pan, Chahat Raj, Ziwei Zhu,
• Abstract summary: Social biases embedded in Large Language Models (LLMs) raise critical concerns.<n>We present the Bias Association Discovery Framework (BADF), a systematic approach for extracting associations between demographic identities and descriptive concepts.<n>Our findings advance the understanding of biases in open-ended generation and provide a scalable tool for identifying and analyzing bias associations in LLMs.
• Score: 1.7373859011890633
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Social biases embedded in Large Language Models (LLMs) raise critical concerns, resulting in representational harms -- unfair or distorted portrayals of demographic groups -- that may be expressed in subtle ways through generated language. Existing evaluation methods often depend on predefined identity-concept associations, limiting their ability to surface new or unexpected forms of bias. In this work, we present the Bias Association Discovery Framework (BADF), a systematic approach for extracting both known and previously unrecognized associations between demographic identities and descriptive concepts from open-ended LLM outputs. Through comprehensive experiments spanning multiple models and diverse real-world contexts, BADF enables robust mapping and analysis of the varied concepts that characterize demographic identities. Our findings advance the understanding of biases in open-ended generation and provide a scalable tool for identifying and analyzing bias associations in LLMs. Data, code, and results are available at https://github.com/JP-25/Discover-Open-Ended-Generation

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