arXiv AI

Unlearning Under Imbalance: Benchmarking Fairness in Multimodal LLM Unlearning

arXiv:2607. 21300v1 Announce Type: cross Abstract: Machine unlearning has emerged as a tool for removing personal data from trained models to comply with recent AI regulations.

arXiv Machine Learning

Aug 10

Let's Unlearn Stereotypes Before Decision-Making: Assessing the Impact of Intrinsic Bias Mitigation on Downstream Fairness in LLMs

arXiv:2509. 16462v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used in high-stakes decision-making systems, where biased predictions can reinforce social and economic disparities.

By Mina Arzaghi, Alireza Dehghanpour Farashah, Florian Carichon, Jean-Fran\c{c}ois Plante, Golnoosh Farnadi

llms rag fine-tuning benchmarks safety

arXiv AI

Jul 10

Adaptive Generation of Bias-Eliciting Questions for LLMs

arXiv:2510. 12857v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are now widely deployed in user-facing applications, reaching hundreds of millions of users worldwide.

By Robin Staab, Jasper Dekoninck, Maximilian Baader, Martin Vechev

llms benchmarks safety

arXiv AI

Jun 8

Automatic Causal Fairness Analysis with LLM-Generated Reporting

arXiv:2604. 27011v2 Announce Type: replace-cross Abstract: AutoML, intended as the process of automating the application of machine learning to real-world problems, is a key step for AI popularisation.

By Alessia Berarducci, Eric Rossetto, Alessandro Antonucci, Marco Zaffalon

llms safety

arXiv Machine Learning

Jun 2

RAIGen: Rare Attribute Identification in Text-to-Image Generative Models

arXiv:2602. 06806v2 Announce Type: replace-cross Abstract: Text-to-image diffusion models achieve impressive generation quality but inherit and amplify training-data biases, skewing coverage of semantic attributes.

By Silpa Vadakkeeveetil Sreelatha, Dan Wang, Serge Belongie, Muhammad Awais, Anjan Dutta

diffusion safety

arXiv AI

Jun 2

Perturbation Effects on Accuracy and Fairness among Similar Individuals

arXiv:2404. 01356v3 Announce Type: replace-cross Abstract: Deep neural networks are vulnerable to adversarial perturbations that can simultaneously degrade prediction robustness and individual fairness across diverse application settings.

By Xuran Li, Hao Xue, Peng Wu, Xingjun Ma, Zhen Zhang, Huaming Chen, Flora D. Salim

safety

arXiv Machine Learning

Jun 2

KG-FairDiff: Knowledge Graph-Guided Prompt Refinement for Demographically Fair Text-to-Image Generation

arXiv:2606. 01282v1 Announce Type: cross Abstract: Text-to-Image (TTI) systems are now everyday infrastructure for journalism, education, advertising, and public communication, and the demographic and cultural stereotypes they inherit from training data (rendering women, people of colour, older adults, and non-Western cultures as under-represented or caricatured) become a population-level harm at deployment scale.

By Farbod Davoodi, Seyed Reza Tavakoli Shiyadeh, Pooria Safaei, Sana Harighi, Parsa Gholami, Amirali Amini, Kimia Vanaei, Emad Firoozi, Parham Abed Azad, Babak Khalaj, Siavash Ahmadi, Amir Hossein Payberah, Mohammad Hossein Rohban, Soheil Kolouri, Ali Diba

llms diffusion safety

arXiv AI

Jul 31

BioPro: Towards Difference-Aware Gender Fairness for Vision-Language Models

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

llms rag diffusion multimodal safety

Hugging Face Trending Papers

Aug 4

Does Forgetting Transfer Across Modalities? A Real-World Benchmark for Cross-Modal Knowledge Unlearning Evaluation

Vision-Language Models (VLMs), like Large Language Models (LLMs), may memorize sensitive, copyrighted, or harmful knowledge from their pretraining corpora. Removing such knowledge is essential for building trustworthy AI systems.

llms multimodal benchmarks

arXiv AI

Aug 5

Does Forgetting Transfer Across Modalities? A Real-World Benchmark for Cross-Modal Knowledge Unlearning Evaluation

arXiv:2608. 03791v1 Announce Type: new Abstract: Vision-Language Models (VLMs), like Large Language Models (LLMs), may memorize sensitive, copyrighted, or harmful knowledge from their pretraining corpora.

By Chunlin Liu, Junnian Chen, Haitong Jiang, Jianyu Zhao, Yingsen Pang, Jingchen Li, Jiabiao He, Youming Lu, Jinhe Bi, Yuntao Du

llms multimodal benchmarks

arXiv AI

Jun 2

Visual-Noise Guided In-Context Distillation for Multimodal Large Language Model Unlearning

arXiv:2606. 00105v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress on vision-language tasks, but they may also memorize and expose sensitive or restricted knowledge, raising concerns about privacy and broader safety risks.

By Junkai Chen, Yuhao He, Junxiang You, Ruiqi Liu, Chenyu Wang, Shu Wu

llms efficiency multimodal safety

arXiv AI

Jul 1

Perturbation Effects on Robustness and Individual Fairness

arXiv:2404. 01356v4 Announce Type: replace-cross Abstract: Deep neural networks are vulnerable to adversarial perturbations that can simultaneously degrade prediction robustness and individual fairness across diverse application settings.

By Xuran Li, Hao Xue, Peng Wu, Xingjun Ma, Zhen Zhang, Huaming Chen, Flora D. Salim

safety

arXiv AI

Jun 12

MLUBench: A Benchmark for Lifelong Unlearning Evaluation in MLLMs

arXiv:2606. 12809v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are trained on massive multimodal data, making data unlearning increasingly important as data owners may request the removal of specific content.

By He Li, Haoang Chi, Qizhou Wang, Yunxin Mao, Zhiheng Zhang, Jie Tan, Tongliang Liu, Wenjing Yang, Bo Han

llms multimodal benchmarks safety

Related stories