GenderAlign: An Alignment Dataset for Mitigating Gender Bias in Large Language Models
CoRR(2024)
摘要
Large Language Models (LLMs) are prone to generating content that exhibits
gender biases, raising significant ethical concerns. Alignment, the process of
fine-tuning LLMs to better align with desired behaviors, is recognized as an
effective approach to mitigate gender biases. Although proprietary LLMs have
made significant strides in mitigating gender bias, their alignment datasets
are not publicly available. The commonly used and publicly available alignment
dataset, HH-RLHF, still exhibits gender bias to some extent. There is a lack of
publicly available alignment datasets specifically designed to address gender
bias. Hence, we developed a new dataset named GenderAlign, aiming at mitigating
a comprehensive set of gender biases in LLMs. This dataset comprises 8k
single-turn dialogues, each paired with a "chosen" and a "rejected" response.
Compared to the "rejected" responses, the "chosen" responses demonstrate lower
levels of gender bias and higher quality. Furthermore, we categorized the
gender biases in the "rejected" responses of GenderAlign into 4 principal
categories. The experimental results show the effectiveness of GenderAlign in
reducing gender bias in LLMs.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要