Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias

• URL: http://arxiv.org/abs/2212.10678v3
• Date: Mon, 21 Oct 2024 03:55:36 GMT
• Title: Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias
• Authors: Yuen Chen, Vethavikashini Chithrra Raghuram, Justus Mattern, Rada Mihalcea, Zhijing Jin,
• Abstract summary: We introduce a causal formulation for bias measurement in generative language models. We propose a benchmark called OccuGender, with a bias-measuring procedure to investigate occupational gender bias. The results show that these models exhibit substantial occupational gender bias.
• Score: 33.99768156365231
• License:
• Abstract: Generated texts from large language models (LLMs) have been shown to exhibit a variety of harmful, human-like biases against various demographics. These findings motivate research efforts aiming to understand and measure such effects. This paper introduces a causal formulation for bias measurement in generative language models. Based on this theoretical foundation, we outline a list of desiderata for designing robust bias benchmarks. We then propose a benchmark called OccuGender, with a bias-measuring procedure to investigate occupational gender bias. We test several state-of-the-art open-source LLMs on OccuGender, including Llama, Mistral, and their instruction-tuned versions. The results show that these models exhibit substantial occupational gender bias. Lastly, we discuss prompting strategies for bias mitigation and an extension of our causal formulation to illustrate the generalizability of our framework. Our code and data https://github.com/chenyuen0103/gender-bias.

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