White Men Lead, Black Women Help? Benchmarking Language Agency Social Biases in LLMs

• URL: http://arxiv.org/abs/2404.10508v4
• Date: Thu, 24 Oct 2024 17:43:28 GMT
• Title: White Men Lead, Black Women Help? Benchmarking Language Agency Social Biases in LLMs
• Authors: Yixin Wan, Kai-Wei Chang,
• Abstract summary: Social biases can manifest in language agency. We introduce the novel Language Agency Bias Evaluation benchmark. We unveil language agency social biases in 3 recent Large Language Model (LLM)-generated content.
• Score: 58.27353205269664
• License:
• Abstract: Social biases can manifest in language agency. While several studies approached agency-related bias in human-written language, very limited research has investigated such biases in Large Language Model (LLM)-generated content. In addition, previous works often rely on string-matching techniques to identify agentic and communal words within texts, which fall short of accurately classifying language agency. We introduce the novel Language Agency Bias Evaluation (LABE) benchmark, which comprehensively evaluates biases in LLMs by analyzing agency levels attributed to different demographic groups in model generations. LABE leverages 5,400 template-based prompts, an accurate agency classifier, and corresponding bias metrics to test for gender, racial, and intersectional language agency biases in LLMs on 3 text generation tasks: biographies, professor reviews, and reference letters. We also contribute the Language Agency Classification (LAC) dataset, consisting of 3,724 agentic and communal sentences. Using LABE, we unveil language agency social biases in 3 recent LLMs: ChatGPT, Llama3, and Mistral. We observe that: (1) LLM generations tend to demonstrate greater gender bias than human-written texts; (2) Models demonstrate remarkably higher levels of intersectional bias than the other bias aspects. Those who are at the intersection of gender and racial minority groups--such as Black females--are consistently described by texts with lower levels of agency, aligning with real-world social inequalities; (3) Among the 3 LLMs investigated, Llama3 demonstrates the greatest overall bias; (4) Not only does prompt-based mitigation fail to resolve language agency bias in LLMs, but it frequently leads to the exacerbation of biases in generated texts.

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