Adapts multi-layer token-level Mahalanobis distance with supervised linear regression to yield improved uncertainty scores for LLM truthfulness tasks.
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Introduces a unified framework integrating uncertainty estimation, calibration, and tool-based abstention for reliable code predictions in language models.
Proposes a two-stage on-the-fly input adaptation framework to reduce mispredictions in code language models across understanding tasks without retraining or additional supervision.
LLMs show improved accuracy on gastroenterology questions but remain overconfident in self-reported certainty across commercial, open-source, and quantized variants.
citing papers explorer
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Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models
Adapts multi-layer token-level Mahalanobis distance with supervised linear regression to yield improved uncertainty scores for LLM truthfulness tasks.
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When to Answer and When to Defer: A Decision Framework for Reliable Code Predictions
Introduces a unified framework integrating uncertainty estimation, calibration, and tool-based abstention for reliable code predictions in language models.
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On-the-Fly Input Adaptation for Reliable Code Intelligence
Proposes a two-stage on-the-fly input adaptation framework to reduce mispredictions in code language models across understanding tasks without retraining or additional supervision.
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Self-Reported Confidence of Large Language Models in Gastroenterology: Analysis of Commercial, Open-Source, and Quantized Models
LLMs show improved accuracy on gastroenterology questions but remain overconfident in self-reported certainty across commercial, open-source, and quantized variants.