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refusal detection

Refusal detection refers to the capability or process in artificial intelligence of identifying when an input warrants declining to generate an answer, as well as the related evaluation task of determining whether a model has issued a refusal. In grounded language systems and retrieval-augmented generation, it specifically denotes a model's ability to recognize flawed, contradictory, ambiguous, or insufficient context that makes a query unanswerable, separating valid inquiries from those that should not be fulfilled. As a foundational component of selective refusal, detection operates as an initial decision mechanism distinct from categorizing specific context errors or formatting responses, playing a critical role in preventing hallucinations, curbing overconfident inaccuracies, and ensuring reliable model alignment under uncertainty.

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RefusalBench: Generative Evaluation of Selective Refusal in Grounded Language Models

RefusalBench: Generative Evaluation of Selective Refusal in Grounded Language Models

Aashiq Muhamed, Leonardo F. R. Ribeiro, Markus Dreyer, Virginia Smith, Mona T. Diab

OrganizationsAmazonCarnegie Mellon University

Why you should read this

Presents RefusalBench, a generative evaluation framework using 176 perturbation strategies to expose how frontier retrieval-augmented models fail to selectively refuse answering from flawed contexts, while demonstrating that this capability requires targeted training rather than increased model scale.

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.

Added

2026-09-30