The DECK taxonomy partitions LLM hallucinations into four detectability regimes using consistency and confidence axes, mapping each to scorer families and identifying a universal blind spot for output-level uncertainty quantification on knowledge-gap inputs.
arXiv preprint arXiv:2405.13845 , year=
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Introduces functional equivalence methods and functional entropy to predict functional correctness of LLM-generated code via uncertainty quantification, outperforming NLI-based baselines in most tested settings.
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DECK: A Consistency x Confidence Taxonomy of LLM Hallucinations
The DECK taxonomy partitions LLM hallucinations into four detectability regimes using consistency and confidence axes, mapping each to scorer families and identifying a universal blind spot for output-level uncertainty quantification on knowledge-gap inputs.
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Functional Entropy: Predicting Functional Correctness in LLM-Generated Code with Uncertainty Quantification
Introduces functional equivalence methods and functional entropy to predict functional correctness of LLM-generated code via uncertainty quantification, outperforming NLI-based baselines in most tested settings.