FASE approximates functional correctness via MST on structural and semantic dissimilarity graphs, reporting 25% better Spearman correlation and 19% better ROCAUC than LLM-based semantic entropy at 0.3% runtime cost on HumanEval and BigCodeBench.
arXiv preprint arXiv:2506.17296
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CLUES decomposes semantic uncertainty into separate ambiguity and instability scores for clinical Text-to-SQL, with instability via Schur complement, outperforming Kernel Language Entropy on failure prediction while enabling diagnostic triage.
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FASE: Fast Adaptive Semantic Entropy for Code Quality
FASE approximates functional correctness via MST on structural and semantic dissimilarity graphs, reporting 25% better Spearman correlation and 19% better ROCAUC than LLM-based semantic entropy at 0.3% runtime cost on HumanEval and BigCodeBench.
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Disentangling Ambiguity from Instability in Large Language Models: A Clinical Text-to-SQL Case Study
CLUES decomposes semantic uncertainty into separate ambiguity and instability scores for clinical Text-to-SQL, with instability via Schur complement, outperforming Kernel Language Entropy on failure prediction while enabling diagnostic triage.