QA-SNNE adds question-answer alignment via bilateral gating to semantic nearest neighbor entropy, yielding higher AUROC for uncertainty detection in surgical VQA models under both standard and rephrased questions.
arXiv preprint arXiv:2506.00245 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
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Ensemble Semantic Entropy improves correlation with code correctness over single-model methods and powers a cascading scaling system that cuts FLOPs by 64.9% while preserving performance on LiveCodeBench.
Mainstream UQ for LLMs reduces to unsupervised clustering of internal generation consistency and therefore cannot detect confident hallucinations or provide reliable safety signals.
citing papers explorer
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When to Trust the Answer: Question-Aligned Semantic Nearest Neighbor Entropy for Safer Surgical VQA
QA-SNNE adds question-answer alignment via bilateral gating to semantic nearest neighbor entropy, yielding higher AUROC for uncertainty detection in surgical VQA models under both standard and rephrased questions.
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Ensemble-Based Uncertainty Estimation for Code Correctness Estimation
Ensemble Semantic Entropy improves correlation with code correctness over single-model methods and powers a cascading scaling system that cuts FLOPs by 64.9% while preserving performance on LiveCodeBench.
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Position: Uncertainty Quantification in LLMs is Just Unsupervised Clustering
Mainstream UQ for LLMs reduces to unsupervised clustering of internal generation consistency and therefore cannot detect confident hallucinations or provide reliable safety signals.