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cs.CL2025
Towards Reliable Retrieval in RAG Systems for Large Legal Datasets
Markus Reuter, Tobias Lingenberg, Rūta Liepiņa +5
Retrieval-Augmented Generation (RAG) is a promising approach to mitigate hallucinations in Large Language Models (LLMs) for legal applications, but its reliability is critically de…
cs.CL2025
Noiser: Bounded Input Perturbations for Attributing Large Language Models
Mohammad Reza Ghasemi Madani, Aryo Pradipta Gema, Gabriele Sarti +3
Feature attribution (FA) methods are common post-hoc approaches that explain how Large Language Models (LLMs) make predictions. Accordingly, generating faithful attributions that r…