Modeling the full token-by-token trajectory of LLM hidden states with neural ODEs, CDEs, and SDEs improves hallucination detection by over 14% AUC on a constructed true/false benchmark, though gains shrink on QA datasets.
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HD-NDEs: Neural Differential Equations for Hallucination Detection in LLMs
Modeling the full token-by-token trajectory of LLM hidden states with neural ODEs, CDEs, and SDEs improves hallucination detection by over 14% AUC on a constructed true/false benchmark, though gains shrink on QA datasets.