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cs.LG2026
Perturb and Recover: Fine-tuning for Effective Backdoor Removal from CLIP
Naman Deep Singh, Francesco Croce, Matthias Hein
Vision-Language models like CLIP have been shown to be highly effective at linking visual perception and natural language understanding, enabling sophisticated image-text capabilit…
cs.LG2026
On the Out-of-Distribution Generalization of Reasoning in Multimodal LLMs for Simple Visual Planning Tasks
Yannic Neuhaus, Nicolas Flammarion, Matthias Hein +1
Integrating reasoning in large language models and large vision-language models has recently led to significant improvement of their capabilities. However, the generalization of re…
cs.LG2025
Unlearning That Lasts: Utility-Preserving, Robust, and Almost Irreversible Forgetting in LLMs
Naman Deep Singh, Maximilian Müller, Francesco Croce +1
Unlearning in large language models (LLMs) involves precisely removing specific information from a pre-trained model. This is crucial to ensure safety of LLMs by deleting private d…