Disentangling Ambiguity from Instability in Large Language Models: A Clinical Text-to-SQL Case Study

In three linesCLUES, a framework for clinical Text-to-SQL, decomposes semantic uncertainty into ambiguity and instability scores using the Schur complement of a bipartite semantic graph matrix. Tested on AmbigQA/SituatedQA and a clinical benchmark, it outperforms Kernel Language Entropy and enables efficient triage: 51% of errors in 25% of queries.

Summary generated by Claude — human-verified