Title: Revealing the Impacts of In-Context Learning on Gender Bias in Large Vision-Language Models
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Large Vision-Language Models, In-context Learning, Gender Bias, Social Bias

Abstract: In-context learning (ICL) has emerged as a flexible paradigm, enabling large vision-language models (LVLMs) to perform tasks by following patterns demonstrated in context exemplars. While prior work has focused on improving ICL performance across multimodal tasks, little attention has been paid to its potential to amplify societal stereotypes. This study aims to fill this gap by systematically investigating how ICL influences societal biases, with a focus on gender bias, in LVLMs. To this end, we propose a comprehensive evaluation framework comprising six ICL settings and evaluate four LVLMs across two tasks. Our findings indicate that ICL could amplify gender bias, while female-presenting in-context examples generally do not exacerbate bias and may even mitigate it. In contrast, similarity-based retrieval methods, originally designed to improve ICL performance, fail to consistently reduce gender bias in LVLMs. To mitigate gender bias through ICL, we propose a provisional approach that replaces natural in-context images with synthetic ones. This method achieves lower gender bias while maintaining stable performance on standard quality metrics. We advocate for the pre-deployment assessment of gender bias in the context for LVLMs and call for the advancement of ICL strategies to promote fairness on downstream applications.

TL;DR: Revealing how ICL impacts LVLMs and Debiasing LVLMs through ICL.