RefusalBench: Generative Evaluation of Selective Refusal in Grounded
  Language Models
Authors: name: Aashiq Muhamed, name: Leonardo F. R. Ribeiro, name: Markus Dreyer, name: Virginia Smith, name: Mona T. Diab
Organization: Amazon AGI

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-scale study reveals that even frontier models struggle
in this setting, with refusal accuracy dropping below 50% on multi-document
tasks, while exhibiting either dangerous overconfidence or overcaution. Static
benchmarks fail to reliably evaluate this capability, as models exploit
dataset-specific artifacts and memorize test instances. We introduce
RefusalBench, a generative methodology that programmatically creates diagnostic
test cases through controlled linguistic perturbation. Our framework employs
176 distinct perturbation strategies across six categories of informational
uncertainty and three intensity levels. Evaluation of over 30 models uncovers
systematic failure patterns: refusal comprises separable detection and
categorization skills, and neither scale nor extended reasoning improves
performance. We find that selective refusal is a trainable, alignment-sensitive
capability, offering a clear path for improvement. We release two benchmarks --
RefusalBench-NQ (single document) and RefusalBench-GaRAGe (multi-document) --
and our complete generation framework to enable continued, dynamic evaluation
of this critical capability.