3 papers
cs.LG2024
Copyright-Protected Language Generation via Adaptive Model Fusion
Javier Abad, Konstantin Donhauser, Francesco Pinto +1
The risk of language models reproducing copyrighted material from their training data has led to the development of various protective measures. Among these, inference-time strateg…
cs.LG2024
Strong Copyright Protection for Language Models via Adaptive Model Fusion
Javier Abad, Konstantin Donhauser, Francesco Pinto +1
The risk of language models unintentionally reproducing copyrighted material from their training data has led to the development of various protective measures. In this paper, we p…
cs.CV2024
Extracting Training Data from Document-Based VQA Models
Francesco Pinto, Nathalie Rauschmayr, Florian Tramèr +2
Vision-Language Models (VLMs) have made remarkable progress in document-based Visual Question Answering (i.e., responding to queries about the contents of an input document provide…