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Vision-Language Models Suppress Female Representations Under Ambiguous Input

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arXiv Computer Vision
Sep 24

Gender Bias in Vision-Language In-Context Learning

The paper investigates how in‑context learning (ICL) in large vision‑language models (LVLMs) can amplify gender bias. Using the VL‑BICLE framework, the authors show that gendered ICL demonstrations shift model bias toward the demonstrated gender, especially in tasks involving gendered language such as image captioning and pronoun prediction. They find that similarity‑based retrieval does not mitigate this bias and that replacing real images with synthetic ones from stable diffusion reduces bias without hurting caption quality.

By Tong Xiang, Noa Garcia, Yuta Nakashima
llmsdiffusionnlpmultimodalsafety
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arXiv Computer Vision
Sep 16

ViD: Vision-Dominant Gender Bias Mitigation for Large Vision-Language Models

The paper introduces ViD, a vision‑dominant gender bias mitigation framework for large vision‑language models. ViD uses causal analysis of attention patterns and dual mechanisms—backdoor adjustment and refined token selection—to suppress bias while preserving reasoning and generation quality. Experiments show a 14.7% reduction in gender bias on FACET and significant improvements on MS COCO image captioning, all without extra training overhead.

By Zhipeng Zhao, Zhaoqiang Wei, Peishun Liu, Youwei Zhao, Ruichun Tang
llmsmultimodalbenchmarkssafety
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arXiv Computation and Language
Sep 11

Alignment Reduces Expressed but Not Encoded Gender Bias: A Unified Framework and Study

The paper introduces a unified framework that simultaneously measures intrinsic (encoded) and extrinsic (expressed) gender bias in large language models using identical neutral prompts. It finds a consistent link between latent gender information and output bias, but shows that alignment via supervised fine‑tuning reduces expressed bias while leaving internal gender associations largely intact and reactivatable by adversarial prompts. The study also demonstrates that debiasing gains on structured benchmarks may not transfer to realistic tasks such as story generation.

By Nour Bouchouchi, Thibault Laugel, Xavier Renard, Christophe Marsala, Marie-Jeanne Lesot, Marcin Detyniecki
llmsfine-tuningbenchmarkssafety
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arXiv Machine Learning
Oct 9

Addressing Overcommitment in the Reasoning of Gendered Economic Memes under Multimodal Ambiguity

The paper introduces CGER-Net, a context‑grounded multimodal framework designed to reduce biased gendered economic role attribution in image‑text memes. By assessing whether sufficient evidence exists for gendered reasoning, the model gates inference, abstaining when cues are ambiguous and confidently attributing roles when explicit. Evaluation on the EconMeme‑GE dataset shows CGER‑Net cuts gender overcommitment by up to 44% on ambiguous memes while preserving accuracy on clear cases, and human judges find 79% of its rationales epistemically aligned with the evidence.

By Kushal Kanwar, Dushyant Singh Chauhan, Kapil Rana, Gopendra Vikram Singh, Nils Lukas
multimodalsafety
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arXiv AI
Sep 18

How Humans and LLMs Read Gender into "Gender-Neutral" Physical Descriptions

The study introduces GAPA, a dataset of 316 physical attributes with 14,706 gender-association ratings from 304 US annotators, showing that such descriptions carry structured gender associations. It evaluates 16 LLMs, finding they partially mirror human ratings but exhibit biases such as compressed distributions, weaker alignment for men, and asymmetric abstention toward non‑binary identities. A proxy model trained on these data is released and applied to analyze character descriptions in LitBank, illustrating the persistence of gendered interpretations in ostensibly neutral language.

By Yingjia Wan, Lin Lin, Elisa Kreiss
llmssafety
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