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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.20898 |
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| _version_ | 1866913712667885568 |
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| author | Mehner, Luise Fiedler, Lena Alicija Philine Ammon, Sabine Kolossa, Dorothea |
| author_facet | Mehner, Luise Fiedler, Lena Alicija Philine Ammon, Sabine Kolossa, Dorothea |
| contents | The widespread application of Large Language Models (LLMs) involves ethical risks for users and societies. A prominent ethical risk of LLMs is the generation of unfair language output that reinforces or exacerbates harm for members of disadvantaged social groups through gender biases (Weidinger et al., 2022; Bender et al., 2021; Kotek et al., 2023). Hence, the evaluation of the fairness of LLM outputs with respect to such biases is a topic of rising interest. To advance research in this field, promote discourse on suitable normative bases and evaluation methodologies, and enhance the reproducibility of related studies, we propose a novel approach to database construction. This approach enables the assessment of gender-related biases in LLM-generated language beyond merely evaluating their degree of neutralization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20898 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A database to support the evaluation of gender biases in GPT-4o output Mehner, Luise Fiedler, Lena Alicija Philine Ammon, Sabine Kolossa, Dorothea Computation and Language The widespread application of Large Language Models (LLMs) involves ethical risks for users and societies. A prominent ethical risk of LLMs is the generation of unfair language output that reinforces or exacerbates harm for members of disadvantaged social groups through gender biases (Weidinger et al., 2022; Bender et al., 2021; Kotek et al., 2023). Hence, the evaluation of the fairness of LLM outputs with respect to such biases is a topic of rising interest. To advance research in this field, promote discourse on suitable normative bases and evaluation methodologies, and enhance the reproducibility of related studies, we propose a novel approach to database construction. This approach enables the assessment of gender-related biases in LLM-generated language beyond merely evaluating their degree of neutralization. |
| title | A database to support the evaluation of gender biases in GPT-4o output |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2502.20898 |