12 papers
UniProbe: A Learnable Token-Level Hallucination Detector for Large VLMs using Multi-Structural Internal Representations
Dvir Samuel, Guy Bar-Shalom, Fabrizio Frasca +4
Large Vision-Language Models (LVLMs) achieve impressive visual reasoning and dialogue capabilities, yet frequently hallucinate content unsupported by the visual input. Effective mi…
Expert Merging in Sparse Mixture of Experts with Nash Bargaining
Dung V. Nguyen, Anh T. Nguyen, Minh H. Nguyen +6
Existing expert merging strategies for Sparse Mixture of Experts (SMoE) typically rely on input-dependent or input-independent averaging of expert parameters, but often lack a prin…
Go Beyond Your Means: Unlearning with Per-Sample Gradient Orthogonalization
Aviv Shamsian, Eitan Shaar, Aviv Navon +2
Machine unlearning aims to remove the influence of problematic training data after a model has been trained. The primary challenge in machine unlearning is ensuring that the proces…
Adversarial Attacks in Weight-Space Classifiers
Tamir Shor, Ethan Fetaya, Chaim Baskin +1
Implicit Neural Representations (INRs) have been recently garnering increasing interest in various research fields, mainly due to their ability to represent large, complex data in…
Can Modern Vision Models Understand the Difference Between an Object and a Look-alike?
Itay Cohen, Ethan Fetaya, Amir Rosenfeld
Recent advances in computer vision have yielded models with strong performance on recognition benchmarks; however, significant gaps remain in comparison to human perception. One su…
Questioning the Stability of Visual Question Answering
Amir Rosenfeld, Neta Glazer, Ethan Fetaya
Visual Language Models (VLMs) have achieved remarkable progress, yet their reliability under small, meaning-preserving input changes remains poorly understood. We present the first…