CLARA: Clarification of Language Ambiguity through Result Analysis for Natural-Language Cancer Genomics Queries

Abstract:A natural language interface can be used to make cancer genomics databases easier to use, but even if a question is perfectly fluent, its scientific meaning can be ambiguous. We propose CLARA, a framework that represents a question as a typed scientific query specification, considers a few possible interpretations, executes them, and asks for clarification when the estimates diverge. CLARA was assessed on mutation-prevalence contrasts among eight TCGA PanCancer Atlas cohorts and a 30-gene panel. This benchmark consisted of 330 unique executable contrasts varying in mutation scope, assay denominator, and sample context; 115 contrasts were result-sensitive and 215 were result-stable, per the preregistered definition of relative divergence greater than 0.10 or absolute divergence greater than 5 percentage points. An independently implemented pandas execution engine perfectly replicated all 660 results from the SQLite engine. In a separate 120-question LLM-generated, manually vetted language stress test, CLARA recognized all 60 result-sensitive contrasts and needlessly clarified 13 of 60 stable contrasts (accuracy 89.2%, sensitivity/recall 100%, specificity 78.3%). Standalone machine learning had superior overall accuracy (97.5%) but missed one critical contrast. This demonstrates that downstream execution can distinguish consequential from inconsequential ambiguity and reveal an explicit trade-off between safety and burden.

Preprint of an article submitted for consideration in the Pacific Symposium on Biocomputing (PSB 2027). Copyright 2026 World Scientific Publishing Company. this https URL

Genomics (q-bio.GN); Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)

arXiv:2608.05195 [q-bio.GN]

(or arXiv:2608.05195v1 [q-bio.GN] for this version)

https://doi.org/10.48550/arXiv.2608.05195 arXiv-issued DOI via DataCite (pending registration)