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        <title>Evaluating Biases in Context-Dependent Sexual and Reproductive Health Questions</title>
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    <name type="personal">
        <namePart type="given">Sharon</namePart>
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            <namePart type="given">Yun-Nung</namePart>
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    <abstract>Chat-based large language models have the opportunity to empower individuals lacking high-quality healthcare access to receive personalized information across a variety of topics. However, users may ask underspecified questions that require additional context for a model to correctly answer. We study how large language model biases are exhibited through these contextual questions in the healthcare domain. To accomplish this, we curate a dataset of sexual and reproductive healthcare questions (ContextSRH) that are dependent on age, sex, and location attributes. We compare models’ outputs with and without demographic context to determine answer alignment among our contextual questions. Our experiments reveal biases in each of these attributes, where young adult female users are favored.</abstract>
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%0 Conference Proceedings
%T Evaluating Biases in Context-Dependent Sexual and Reproductive Health Questions
%A Levy, Sharon
%A Karver, Tahilin
%A Adler, William
%A Kaufman, Michelle
%A Dredze, Mark
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F levy-etal-2024-evaluating
%X Chat-based large language models have the opportunity to empower individuals lacking high-quality healthcare access to receive personalized information across a variety of topics. However, users may ask underspecified questions that require additional context for a model to correctly answer. We study how large language model biases are exhibited through these contextual questions in the healthcare domain. To accomplish this, we curate a dataset of sexual and reproductive healthcare questions (ContextSRH) that are dependent on age, sex, and location attributes. We compare models’ outputs with and without demographic context to determine answer alignment among our contextual questions. Our experiments reveal biases in each of these attributes, where young adult female users are favored.
%U https://aclanthology.org/2024.findings-emnlp.332
%P 5801-5812

Markdown (Informal)

[Evaluating Biases in Context-Dependent Sexual and Reproductive Health Questions](https://aclanthology.org/2024.findings-emnlp.332) (Levy et al., Findings 2024)

• Evaluating Biases in Context-Dependent Sexual and Reproductive Health Questions (Levy et al., Findings 2024)

ACL

• Sharon Levy, Tahilin Karver, William Adler, Michelle Kaufman, and Mark Dredze. 2024. Evaluating Biases in Context-Dependent Sexual and Reproductive Health Questions. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 5801–5812, Miami, Florida, USA. Association for Computational Linguistics.