Summary of Fairpair: a Robust Evaluation Of Biases in Language Models Through Paired Perturbations, by Jane Dwivedi-yu and Raaz Dwivedi and Timo Schick
FairPair: A Robust Evaluation of Biases in Language Models through Paired Perturbations
by Jane Dwivedi-Yu, Raaz Dwivedi, Timo Schick
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 This research paper proposes FairPair, an evaluation framework designed to assess differential treatment in language models. The framework aims to identify biases that appear in typical usage, rather than just extreme cases. FairPair uses counterfactual pairs, ensuring equivalent comparison by grounding the paired continuations in the same demographic group. Additionally, it factors in the inherent variability of the generation process itself by measuring sampling variability. The paper evaluates several commonly used generative models and finds a bias towards discussing family and hobbies with regard to women. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps ensure that language models are fair and safe for everyone. The problem is that current methods only look at extreme cases, not everyday usage. The new framework, FairPair, fixes this by comparing how models behave in similar situations. It’s like taking a selfie and comparing it to a friend’s selfie – you can see if they’re treated differently. The researchers tested popular language models and found that some are more likely to talk about women’s appearance than men’s. |
Keywords
* Artificial intelligence * Grounding
