Papers by James R. Foulds
GenderAlign: An Alignment Dataset for Mitigating Gender Bias in Large Language Models (2025.acl-long)
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Tao Zhang, Ziqian Zeng, YuxiangXiao YuxiangXiao, Huiping Zhuang, Cen Chen, James R. Foulds, Shimei Pan
| Challenge: | Large Language Models (LLMs) generate content that exhibits gender biases, raising ethical concerns. |
| Approach: | They propose to use a dataset to identify gender biases in Large Language Models (LLMs) this dataset is a "chosen" and "rejected" LLM alignment is an effective approach to mitigate gender bias. |
| Outcome: | The proposed dataset shows that it reduces gender bias and improves quality. |