Summary of Subtle Biases Need Subtler Measures: Dual Metrics For Evaluating Representative and Affinity Bias in Large Language Models, by Abhishek Kumar et al.
Subtle Biases Need Subtler Measures: Dual Metrics for Evaluating Representative and Affinity Bias in Large Language Models
by Abhishek Kumar, Sarfaroz Yunusov, Ali Emami
First submitted to arxiv on: 23 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); 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 A new study on Large Language Models (LLMs) uncovers two subtle biases that can significantly influence their outputs: representative bias, which favors certain identity groups’ experiences, and affinity bias, which evaluates narratives or viewpoints. Researchers introduce novel metrics to measure these biases and develop the Creativity-Oriented Generation Suite (CoGS), a set of open-ended tasks to detect representative and affinity biases in LLMs. The study finds marked representative biases towards identities associated with being white, straight, and men, and distinctive evaluative patterns akin to `bias fingerprints’ in human evaluators. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models can have biases that are not immediately apparent, but still affect their outputs. This research looks at two types of bias: representative bias, which is when the model’s output favors certain identity groups, and affinity bias, which is when the model likes or dislikes certain narratives or viewpoints. The researchers created special tools to measure these biases and tested them on different language models. They found that many popular language models have a bias towards white, straight, and male identities. This study also shows that human evaluators can have similar biases. |
