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
arXiv:2512. 00807v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) inherit significant social biases from their training data, notably in gender representation.
By Yujie Lin, Jiayao Ma, Qingguo Hu, Wenbo Li, Genji Li, Derek Wong, Jinsong Su
arXiv:2512. 08724v3 Announce Type: replace Abstract: Text-to-image (TTI) diffusion models have achieved remarkable visual quality, yet they have been repeatedly shown to exhibit social biases across sensitive attributes such as gender, race and age.
By Manos Plitsis, Giorgos Bouritsas, Vassilis Katsouros, Yannis Panagakis
Melange is a synthetic visual bias benchmark designed to evaluate social biases in multi‑modal large language models (MLLMs). It extends the language‑only BBQ dataset by generating controlled visual scenarios that clearly depict target attributes, and validates images through automated VQA screening and human inspection. Using Melange, 23 open‑source MLLMs were tested in text‑only, image‑only, and image‑with‑context settings, revealing that switching from text to vision reduces fairness by an average of 0.291 and that models often choose stereotypical responses when biased answers are made.
By Razieh Rezaei, Ehsaneddin Asgary, Masoud Jalili Sabet
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
arXiv:2608.21415v1 Announce Type: cross
Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable performance across a wide range of tasks; however, they often inherit social biases fro...
By Yisong Xiao, Aishan Liu, Yongxin Huang, Zonghao Ying, Shiji Zhao, Tianlin Li, Yong Han, Jian Yang, Xianglong Liu
arXiv:2605.31556v2 Announce Type: replace-cross
Abstract: Alignment teaches vision-language models (VLMs) to avoid expressing demographic biases, and when gender is clearly visible they largely succe...
By Arnau Marin-Llobet, Simon Henniger, Mahzarin R. Banaji
arXiv:2605. 08245v4 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) increasingly power high-stakes applications, from medical imaging to autonomous systems, yet they routinely hallucinate, confidently describing content not present in the input.
By Harshvardhan Saini, Samyak Jha, Yiming Tang, Dianbo Liu
The paper introduces SAGE, a lightweight method that guides decoder attention in vision-language models away from prompt‑invariant attention sinks (PIS) toward query‑dependent regions of interest (ROIs). By using token‑aligned ROI masks from vision backbones like CLIP, ViT, and DINOv3, SAGE improves visual grounding and reduces hallucinations across various VLM families. Experiments show consistent gains on public vision‑language benchmarks, especially in fine‑grained visual discrimination tasks where localized evidence is essential.
By Jeonghyo Song, YoungJoon Yoo
arXiv:2507. 18043v2 Announce Type: replace-cross Abstract: Inference-time steering methods offer a lightweight alternative to fine-tuning large language models (LLMs) and vision-language models (VLMs) by modifying internal activations at test time without updating model weights.
By Duy Nguyen, Archiki Prasad, Elias Stengel-Eskin, Mohit Bansal
arXiv:2603. 24058v2 Announce Type: replace-cross Abstract: Object hallucination in Large Vision-Language Models (LVLMs) severely compromises their reliability in real-world applications, posing a critical barrier to their deployment in high-stakes scenarios such as autonomous driving and medical image analysis.
By Han Sun, Qin Li, Peixin Wang, Min Zhang
arXiv:2606. 26387v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) extend large language models (LLMs) with visual perception, enabling joint reasoning over images and text.
By Xi Xiao, Chen Liu, Chih-Ting Liao, Yunbei Zhang, Qizhen Lan, Yuxiang Wei, Lin Zhao, Janet Wang, Jianyang Gu, Muchao Ye, Tianyang Wang, Hao Xu