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refusal accuracy

Refusal accuracy is an evaluation metric that measures the capability of an artificial intelligence model, particularly within retrieval-augmented generation and context-grounded systems, to correctly decline to answer when the supplied context is missing, insufficient, conflicting, or otherwise unreliable. Rather than generating fabricated answers or relying on invalid source material, a model with high refusal accuracy successfully recognizes unanswerable scenarios and withholds a direct answer, while also avoiding improper refusals when valid and sufficient information is available. This metric serves as a key indicator of model calibration, safety, and reliability, reflecting a system balance between avoiding overconfident hallucinations and preventing overly cautious non-responses.

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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