Summary of Locating and Mitigating Gender Bias in Large Language Models, by Yuchen Cai and Ding Cao and Rongxi Guo and Yaqin Wen and Guiquan Liu and Enhong Chen
Locating and Mitigating Gender Bias in Large Language Models
by Yuchen Cai, Ding Cao, Rongxi Guo, Yaqin Wen, Guiquan Liu, Enhong Chen
First submitted to arxiv on: 21 Mar 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 proposed study tackles the issue of biases and stereotypes in large language models (LLMs) by integrating location and mitigation processes within a unified framework. The researchers employ causal mediation analysis to identify the effects of different components’ activation on bias, and propose the Least Square Debias Method (LSDM), a knowledge-editing approach for mitigating gender bias in occupational pronouns. Experimental results demonstrate that the primary contributors to gender bias are specific modules in the LLM, and LSDM effectively reduces bias while preserving model capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models learn facts and human cognition from extensive corpora, but this process can lead to biases and stereotypes. The study proposes a unified framework for locating and mitigating bias. The researchers use causal mediation analysis to understand how different components of the LLM contribute to bias. They also develop the Least Square Debias Method (LSDM) to reduce gender bias in occupational pronouns. This method is compared to two baselines on several datasets, showing that LSDM effectively reduces bias while preserving model capabilities. |
