Summary of Genderalign: An Alignment Dataset For Mitigating Gender Bias in Large Language Models, by Tao Zhang et al.
GenderAlign: An Alignment Dataset for Mitigating Gender Bias in Large Language Models
by Tao Zhang, Ziqian Zeng, Yuxiang Xiao, Huiping Zhuang, Cen Chen, James Foulds, Shimei Pan
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper aims to address the significant ethical concern of large language models (LLMs) generating content with gender biases by developing a new publicly available alignment dataset, GenderAlign. This dataset is designed to mitigate a comprehensive set of gender biases in LLMs and consists of 8k single-turn dialogues paired with “chosen” and “rejected” responses. The “chosen” responses demonstrate lower levels of gender bias and higher quality compared to the “rejected” responses. Furthermore, the paper categorizes the gender biases in the “rejected” responses into four principal categories. Experimental results show the effectiveness of GenderAlign in reducing gender bias in LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The goal of this study is to make large language models less likely to produce biased content. The researchers created a new dataset called GenderAlign that helps train these models to be more fair. This dataset has 8,000 conversations where each conversation has two responses: one that’s considered good and another that’s not as good because it shows gender bias. By using this dataset, the study found that language models can learn to produce less biased content. |
Keywords
» Artificial intelligence » Alignment
