CORTEX: Token-Level Hallucination Detection in RAG via Comparative Internal Representations
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 effect, CORTEX compares internal representations of a large language model (LLM) under two conditions: with and without the retrieved documents. Ins...
| Authors | Kazuaki Furumai, Shuichiro Haruta, Kazunori Matsumoto et al. |
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| Primary category | cs.CL |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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