Disentangling Ambiguity from Instability in Large Language Models: A Clinical Text-to-SQL Case Study
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arXiv
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917507249471488 |
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| author | Ziletti, Angelo D'Ambrosi, Leonardo |
| author_facet | Ziletti, Angelo D'Ambrosi, Leonardo |
| contents | Deploying large language models for clinical Text-to-SQL requires distinguishing two qualitatively different causes of output diversity: (i) input ambiguity that should trigger clarification, and (ii) model instability that should trigger human review. We propose CLUES, a framework that models Text-to-SQL as a two-stage process (interpretations --> answers) and decomposes semantic uncertainty into an ambiguity score and an instability score. The instability score is computed via the Schur complement of a bipartite semantic graph matrix. Across AmbigQA/SituatedQA (gold interpretations) and a clinical Text-to-SQL benchmark (known interpretations), CLUES improves failure prediction over state-of-the-art Kernel Language Entropy. In deployment settings, it remains competitive while providing a diagnostic decomposition unavailable from a single score. The resulting uncertainty regimes map to targeted interventions - query refinement for ambiguity, model improvement for instability. The high-ambiguity/high-instability regime contains 51% of errors while covering 25% of queries, enabling efficient triage. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_12015 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Disentangling Ambiguity from Instability in Large Language Models: A Clinical Text-to-SQL Case Study Ziletti, Angelo D'Ambrosi, Leonardo Computation and Language Deploying large language models for clinical Text-to-SQL requires distinguishing two qualitatively different causes of output diversity: (i) input ambiguity that should trigger clarification, and (ii) model instability that should trigger human review. We propose CLUES, a framework that models Text-to-SQL as a two-stage process (interpretations --> answers) and decomposes semantic uncertainty into an ambiguity score and an instability score. The instability score is computed via the Schur complement of a bipartite semantic graph matrix. Across AmbigQA/SituatedQA (gold interpretations) and a clinical Text-to-SQL benchmark (known interpretations), CLUES improves failure prediction over state-of-the-art Kernel Language Entropy. In deployment settings, it remains competitive while providing a diagnostic decomposition unavailable from a single score. The resulting uncertainty regimes map to targeted interventions - query refinement for ambiguity, model improvement for instability. The high-ambiguity/high-instability regime contains 51% of errors while covering 25% of queries, enabling efficient triage. |
| title | Disentangling Ambiguity from Instability in Large Language Models: A Clinical Text-to-SQL Case Study |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2602.12015 |