Detecting Hallucinations in Retrieval-Augmented Generation through Grounding-Aware Sensitivity by Perturbation (GASP)
arXiv:2607.04223v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) reduces but does not eliminate hallucination, and existing detectors return a single answer-level score that does not indicate which sentence is unsupported, or why. To close this gap, we introduce Grounding-Aware Sensitivity by Perturbation (GASP), a span-level detector that scores each answer sentence by how strongly its likelihood depends on the retrieved evidence, a quantity we term grounding sensitivity. GASP holds the answer fixed and re-scores it under the full context, under no context, and with each
The proliferation of RAG systems highlights the urgent need for robust hallucination detection to ensure reliability and trustworthiness in AI applications.
Improving hallucination detection directly addresses a critical weakness in current AI systems, enhancing their utility and broadening their deployment in sensitive applications.
The ability to pinpoint specific unsupported sentences rather than just a general score allows for more targeted and effective mitigation of RAG hallucinations.
- · AI developers
- · RAG system users
- · Enterprise AI
- · AI systems with poor grounding
- · Credibility of unverified AI outputs
More reliable RAG systems become available, increasing user trust and adoption.
Enterprises integrate RAG more deeply into mission-critical workflows, automating tasks previously too risky for AI.
The enhanced trustworthiness of AI outputs accelerates the collapse of certain white-collar workflows, as autonomous agents become more viable.
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Read at arXiv cs.AI