Mitigating Social Bias in Large Language Models: A Multi-Objective Approach within a Multi-Agent Framework
This addresses the issue of biased outputs in LLMs for users and developers, offering a method that mitigates bias without significant performance loss, though it is incremental as it builds on existing debiasing techniques.
The paper tackles the problem of social bias in large language models by proposing a multi-objective approach within a multi-agent framework (MOMA), which reduces bias scores by up to 87.7% with only a marginal performance degradation of up to 6.8% in downstream tasks.
Natural language processing (NLP) has seen remarkable advancements with the development of large language models (LLMs). Despite these advancements, LLMs often produce socially biased outputs. Recent studies have mainly addressed this problem by prompting LLMs to behave ethically, but this approach results in unacceptable performance degradation. In this paper, we propose a multi-objective approach within a multi-agent framework (MOMA) to mitigate social bias in LLMs without significantly compromising their performance. The key idea of MOMA involves deploying multiple agents to perform causal interventions on bias-related contents of the input questions, breaking the shortcut connection between these contents and the corresponding answers. Unlike traditional debiasing techniques leading to performance degradation, MOMA substantially reduces bias while maintaining accuracy in downstream tasks. Our experiments conducted on two datasets and two models demonstrate that MOMA reduces bias scores by up to 87.7%, with only a marginal performance degradation of up to 6.8% in the BBQ dataset. Additionally, it significantly enhances the multi-objective metric icat in the StereoSet dataset by up to 58.1%. Code will be made available at https://github.com/Cortantse/MOMA.