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.
Uncer- tainty quantification for hallucination detection in large language models: Foundations, methodology, and future directions
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This paper introduces a systems-level conceptual framing and a three-level taxonomy (intra-model, system-level, socio-technical) for uncertainty propagation in compound LLM applications, along with engineering insights and open challenges.
LLMs need metacognition to align expressed uncertainty with their actual knowledge boundaries, moving beyond knowledge expansion to reduce confident errors.
Empirical study across multiple benchmarks finds the link between uncertainty estimators and LLM hallucinations is highly variable and often weak.
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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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Uncertainty Propagation in LLM-Based Systems
This paper introduces a systems-level conceptual framing and a three-level taxonomy (intra-model, system-level, socio-technical) for uncertainty propagation in compound LLM applications, along with engineering insights and open challenges.
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Hallucinations Undermine Trust; Metacognition is a Way Forward
LLMs need metacognition to align expressed uncertainty with their actual knowledge boundaries, moving beyond knowledge expansion to reduce confident errors.
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Evaluating the Relevance of Uncertainty Estimators for LLM Hallucination
Empirical study across multiple benchmarks finds the link between uncertainty estimators and LLM hallucinations is highly variable and often weak.