RefusalBench: Generative Evaluation of Selective Refusal in Grounded
  Language Models
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.
68f1bb1f6e0bef323a68fe35
Aashiq Muhamed
68f1bb1f6e0bef323a68fe36
Leonardo F. R. Ribeiro
68f1bb1f6e0bef323a68fe37
Markus Dreyer
68f1bb1f6e0bef323a68fe38
Virginia Smith
68f1bb1f6e0bef323a68fe39
Mona T. Diab
RefusalBench: Generative Evaluation of Selective Refusal in Grounded
  Language Models
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.
RefusalBench evaluates the selective refusal capability of language models in RAG systems using programmatically generated test cases, revealing systematic failure patterns and offering a path for improvement.
RAG systems language models selective refusal refusal accuracy dangerous overconfidence overcaution static benchmarks generative methodology controlled linguistic perturbation perturbation strategies informational uncertainty intensity levels RefusalBench-NQ RefusalBench-GaRAGe