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October 9, 2025Open Access

FairReason: Balancing Reasoning and Social Bias in MLLMs

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Benchmarking bias-mitigation strategies and exploring the reasoning versus bias trade-off in MLLMs.

A 1:4 mix using reinforcement learning reduces stereotype scores by 10%, while keeping 88% of reasoning accuracy.
We benchmark three bias-mitigation techniques: supervised fine-tuning, knowledge distillation, and reinforcement learning.
The findings provide a framework for balancing reasoning and bias in multimodal large language models.
Exploring these strategies reveals a method to improve logical performance without enhancing bias.

Pan et al. (2025) studied this question.

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October 9, 2025Open Access

View Full Paper

Ask AI

Bookmark

Authors

Benchmarking bias-mitigation strategies and exploring the reasoning versus bias trade-off in MLLMs.

A 1:4 mix using reinforcement learning reduces stereotype scores by 10%, while keeping 88% of reasoning accuracy.
We benchmark three bias-mitigation techniques: supervised fine-tuning, knowledge distillation, and reinforcement learning.
The findings provide a framework for balancing reasoning and bias in multimodal large language models.
Exploring these strategies reveals a method to improve logical performance without enhancing bias.

Pan et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark