Summary of Identifying and Mitigating Social Bias Knowledge in Language Models, by Ruizhe Chen et al.
Identifying and Mitigating Social Bias Knowledge in Language Models
by Ruizhe Chen, Yichen Li, Jianfei Yang, Joey Tianyi Zhou, Jian Wu, Zuozhu Liu
First submitted to arxiv on: 7 Aug 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 The paper proposes a novel approach to debiasing large language models (LLMs) by fine-tuning individual social biases without compromising their overall performance. The authors introduce BiaScope, a new benchmark for bias mitigation that assesses knowledge retention and generalization using newly constructed datasets and metrics. They also present Fairness Stamp (FAST), a method that identifies the decisive layer responsible for storing social biases and calibrates its outputs to achieve balance between debiasing and knowledge preservation. The authors demonstrate that FAST outperforms state-of-the-art baselines in debiasing performance while maintaining the models’ capability for knowledge retention and downstream predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making language models fairer by reducing bias. Right now, most methods try to make sure the model treats people equally, but this can still lead to unfair or wrong answers because they don’t consider individual facts. The authors create a new way to test how well debiasing works called BiaScope, and then propose an approach called Fairness Stamp (FAST) that helps fine-tune individual biases without sacrificing the model’s ability to learn. |
Keywords
» Artificial intelligence » Fine tuning » Generalization
