FEPoID automatically selects optimal or near-optimal intermediate layers for hallucination detection across LLM architectures and tasks, outperforming prior criteria and baselines, with an added truncation step that further improves performance.
The Illusion of Progress: Re-evaluating Hallucination Detection in LLM s
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CORTEX detects token-level hallucinations in RAG via comparative internal representations, information propagation, and smoothing, reporting gains on two benchmarks with three LLMs.
A factorized study finds raw hidden states and attention features hard to beat in-domain for LLM uncertainty probes, but structured compressed features are more robust under distribution shift, with pretrained probes transferring to open-ended generation.
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Automatic Layer Selection for Hallucination Detection
FEPoID automatically selects optimal or near-optimal intermediate layers for hallucination detection across LLM architectures and tasks, outperforming prior criteria and baselines, with an added truncation step that further improves performance.