Vacuity-based OOD detection in evidential deep learning is highly sensitive to class cardinality differences between ID and OOD, which can artificially inflate AUROC and AUPR without any change in model predictions.
The llama 3 herd of models
4 Pith papers cite this work. Polarity classification is still indexing.
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PathCal calibrates reasoning paths by type-aware soft rebalancing of reflection-marker logits at uncertain states, yielding better efficiency-performance trade-offs on six benchmarks.
Geometry-calibrated conformal abstention lets language models abstain from uncertain queries with finite-sample guarantees on both participation rate and conditional correctness of answers.
MAP-Elites maps continuous vulnerability topologies in three LLMs, achieving up to 63% behavioral coverage and 370 niches with model-specific signatures that existing attack methods cannot provide.
citing papers explorer
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Rethinking Vacuity for OOD Detection in Evidential Deep Learning
Vacuity-based OOD detection in evidential deep learning is highly sensitive to class cardinality differences between ID and OOD, which can artificially inflate AUROC and AUPR without any change in model predictions.
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PathCal: State-Aware Reflection-Marker Calibration for Efficient Reasoning
PathCal calibrates reasoning paths by type-aware soft rebalancing of reflection-marker logits at uncertain states, yielding better efficiency-performance trade-offs on six benchmarks.
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Geometry-Calibrated Conformal Abstention for Language Models
Geometry-calibrated conformal abstention lets language models abstain from uncertain queries with finite-sample guarantees on both participation rate and conditional correctness of answers.
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Manifold of Failure: Behavioral Attraction Basins in Language Models
MAP-Elites maps continuous vulnerability topologies in three LLMs, achieving up to 63% behavioral coverage and 370 niches with model-specific signatures that existing attack methods cannot provide.