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cs.CL2025
RefusalBench: Generative Evaluation of Selective Refusal in Grounded Language Models
Aashiq Muhamed, Leonardo F. R. Ribeiro, Markus Dreyer +2
The ability of language models in RAG systems to selectively refuse to answer based on flawed context is critical for safety, yet remains a significant failure point. Our large-sca…
cs.CL2025
NeoQA: Evidence-based Question Answering with Generated News Events
Max Glockner, Xiang Jiang, Leonardo F. R. Ribeiro +2
Evaluating Retrieval-Augmented Generation (RAG) in large language models (LLMs) is challenging because benchmarks can quickly become stale. Questions initially requiring retrieval…