Summary of Assessing Gender Bias in Llms: Comparing Llm Outputs with Human Perceptions and Official Statistics, by Tetiana Bas
Assessing Gender Bias in LLMs: Comparing LLM Outputs with Human Perceptions and Official Statistics
by Tetiana Bas
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 study examines gender bias in large language models by comparing their gender perception to that of human respondents, U.S. Bureau of Labor Statistics data, and a 50% no-bias benchmark. The researchers created a new evaluation set using occupational data and role-specific sentences to prevent data leakage and test set contamination. Five LLMs were tested to predict the gender for each role using single-word answers. The study used Kullback-Leibler (KL) divergence to compare model outputs with human perceptions, statistical data, and the 50% neutrality benchmark. All LLMs showed significant deviation from gender neutrality and aligned more with statistical data, still reflecting inherent biases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have a problem – they’re biased against one half of humanity. The study looks at how well these AI systems can predict if someone is male or female based on their job title. They compared the AI’s answers to what humans think and found that the AI systems are not very good at being neutral. Instead, they follow the statistical patterns in the data they were trained on, which means they’re just as biased as the people who created them. |
