Core Concepts
Integrating the processes of locating and mitigating gender bias within a unified framework is essential for effective debiasing in large language models.
Abstract
The content discusses the importance of addressing gender bias in large language models through a unified framework. It highlights the challenges posed by biases acquired during model training and the limitations of current debiasing methods. The study proposes the LSDM (Least Square Debias Method) to effectively mitigate gender bias, focusing on occupational pronouns. Experimental results demonstrate that LSDM outperforms other baselines in reducing gender bias while preserving model capabilities across various datasets.
Structure:
- Introduction to Gender Bias in Large Language Models
- Existing Research on Bias Identification and Mitigation
- Proposed Unified Framework for Locating and Mitigating Gender Bias
- Causal Tracing of Gender Bias Mechanisms
- Least Square Debias Method (LSDM) for Occupational Pronouns
- Experimental Results on Gender Bias Datasets
- Evaluation on Model Proficiency Testing Datasets
Stats
"The experimental results indicate that the primary contributors to gender bias are the bottom MLP modules acting on the last token of occupational pronouns."
"LSDM mitigates gender bias in the model more effectively than other baselines."
Quotes
"Bias refers to the existence of consistent inaccuracies, misattributions, or erroneous perceptions leading to a preference for specific groups or concepts."
"Our main contributions are as follows: We trace the causal effects of different components’ activation within a large language model using causal mediation analysis to measure the magnitude."