arXiv:2606.31033v1 Announce Type: new Abstract: In this paper, we propose CORTEX, a token-level hallucination detection method for Retrieval-Augmented Generation (RAG). In long-form RAG outputs, hallucinations often arise in localized spans rather than throughout an entire response. CORTEX therefore identifies ungrounded content at the token level, enabling fine-grained localization of hallucinations. The key intuition behind CORTEX is that tokens grounded in retrieved documents should be more strongly influenced by those documents than hallucinated tokens. To capture this document-induced eff
The proliferation of Retrieval-Augmented Generation (RAG) systems necessitates robust methods for ensuring factual accuracy, making hallucination detection a critical and timely problem.
Sophisticated audiences developing or deploying RAG systems must address hallucination to maintain trust and utility, impacting AI system reliability and adoption.
This research provides a more granular, token-level approach to identifying hallucinations in RAG outputs, moving beyond document-level verification.
- · AI developers
- · RAG system users
- · Data scientists
- · Content integrity platforms
- · Platforms with high hallucination rates
- · Generic hallucination detection methods
Improved reliability and trustworthiness of AI-generated content, especially for high-stakes applications.
Faster debugging and refinement of RAG models, leading to more efficient AI development cycles.
Enhanced confidence in autonomous AI agents that rely on RAG, potentially accelerating their deployment in enterprise settings.
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Read at arXiv cs.CL