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

Kai-Wei Chang Yixin Wan, 2024.

Best paper at TrustNLP workshop at NAACL

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Abstract

Language agency is an important aspect of evaluating social biases in texts. 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 research often relies 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. To build better and more accurate automated agency classifiers, we also contribute and release the Language Agency Classification (LAC) dataset, consisting of 3,724 agentic and communal sentences. Using LABE, we unveil previously under-explored language agency social biases in 3 recent LLMs: ChatGPT, Llama3, and Mistral. We observe that: (1) For the same text category, LLM generations demonstrate higher levels of gender bias than human-written texts; (2) On most generation tasks, models show 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; (3) Among the 3 LLMs investigated, Llama3 demonstrates greatest overall bias in language agency; (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.

Bib Entry

@inproceedings{wan2024white,
  title = {White Men Lead, Black Women Help? Benchmarking Language Agency Social Biases in LLMs},
  author = {Yixin Wan, Kai-Wei Chang},
  year = {2024}
}