Summary of Bias Similarity Across Large Language Models, by Hyejun Jeong et al.
Bias Similarity Across Large Language Models
by Hyejun Jeong, Shiqing Ma, Amir Houmansadr
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 Machine learning educators can benefit from understanding the paper’s contributions to the debate on bias in Large Language Models (LLMs). The study analyzes 13 LLMs from five families, evaluating bias through output distributions across multiple dimensions using two datasets. Results show that fine-tuning has minimal impact and proprietary models tend to overly respond as unknowns to minimize bias, compromising accuracy and utility. Open-source models demonstrate fairness comparable to proprietary ones, challenging assumptions about larger, closed-source models being inherently less biased. Bias scores for disambiguated questions are more extreme, raising concerns about reverse discrimination. The findings emphasize the need for improved bias mitigation strategies and comprehensive evaluation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about making sure computer programs called Large Language Models don’t make unfair decisions. These programs can affect important choices in society, so it’s crucial they’re fair and unbiased. The researchers looked at 13 of these models to see how biased they were and found some surprising things. They discovered that just fine-tuning the models didn’t help much, and some models tried to hide their biases by saying “I don’t know” more often. This actually made them less accurate and useful. The study also found that open-source models can be as fair as closed-source ones, which challenges common assumptions. Overall, the paper highlights the need for better ways to prevent bias in these programs. |
Keywords
» Artificial intelligence » Fine tuning » Machine learning
