Summary of Gender Bias in Llm-generated Interview Responses, by Haein Kong et al.
Gender Bias in LLM-generated Interview Responses
by Haein Kong, Yongsu Ahn, Sangyub Lee, Yunho Maeng
First submitted to arxiv on: 28 Oct 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 This study evaluates three large language models (LLMs), GPT-3.5, GPT-4, and Claude, to investigate their performance in generating job-related texts that are free from gender bias. The researchers found that LLM-generated interview responses consistently exhibit gender bias, aligning with traditional gender stereotypes and the prevalence of certain jobs. Specifically, the study reveals that models tend to produce answers that conform to dominant gender norms, highlighting the need for a thoughtful approach to mitigate these biases in related applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well large language models do at generating job-related texts without showing bias towards one gender over another. The researchers found that these models often make assumptions about what jobs are more suitable for men or women, which is not fair. They think this could be a problem in areas like job interviews and education. Overall, the study shows how important it is to consider these biases when using language models to help people with tasks. |
Keywords
* Artificial intelligence * Claude * Gpt
