2 citations · 4 across the 18 of their papers we have counts for
28 papers
Unlearning Under Imbalance: Benchmarking Fairness in Multimodal LLM Unlearning
Lorenzo Orsingher, Thomas De Min, Massimiliano Mancini +2
Machine unlearning has emerged as a tool for removing personal data from trained models to comply with recent AI regulations. To evaluate unlearning effectiveness in multimodal lar…
Personalizing MLLMs via Reinforced Multimodal Reference Game
Deepayan Das, Davide Talon, Yiming Wang +2
Personalizing Multimodal Large Language Models (MLLMs) aims to recognize users' unique concepts from visual data and provide personalized responses. Although prior work has shown t…
DataComp-VLM: Improved Open Datasets for Vision-Language Models
Matteo Farina, Vishaal Udandarao, Thao Nguyen +33
Building performant Vision-Language Models (VLMs) requires carefully curating large-scale training datasets, yet the community lacks systematic benchmarks for evaluating such curat…
Thinking Past the Answer: Evaluating Harmful Overthinking in Large Reasoning Models
Simone Caldarella, Davide Talon, Rahaf Aljundi +2
Large Reasoning Models (LRMs) improve performance by generating explicit intermediate reasoning traces through increased test-time compute, yet the assumption that longer reasoning…
From Weights to Concepts: Data-Free Interpretability of CLIP via Singular Vector Decomposition
Francesco Gentile, Nicola Dall'Asen, Francesco Tonini +3
As vision-language models are deployed at scale, understanding their internal mechanisms becomes increasingly critical. Existing interpretability methods predominantly rely on acti…
SEM: Sparse Embedding Modulation for Post-Hoc Debiasing of Vision-Language Models
Quentin Guimard, Federico Bartsch, Simone Caldarella +3
Models that bridge vision and language, such as CLIP, are key components of multimodal AI, yet their large-scale, uncurated training data introduce severe social and spurious biase…