[2603.01795] PleaSQLarify: Visual Pragmatic Repair for Natural Language Database Querying

Computer Science > Human-Computer Interaction arXiv:2603.01795 (cs) [Submitted on 2 Mar 2026] Title:PleaSQLarify: Visual Pragmatic Repair for Natural Language Database Querying Authors:Robin Shing Moon Chan, Rita Sevastjanova, Mennatallah El-Assady View a PDF of the paper titled PleaSQLarify: Visual Pragmatic Repair for Natural Language Database Querying, by Robin Shing Moon Chan and 2 other authors View PDF HTML (experimental) Abstract:Natural language database interfaces broaden data access, yet they remain brittle under input ambiguity. Standard approaches often collapse uncertainty into a single query, offering little support for mismatches between user intent and system interpretation. We reframe this challenge through pragmatic inference: while users economize expressions, systems operate on priors over the action space that may not align with the users'. In this view, pragmatic repair -- incremental clarification through minimal interaction -- is a natural strategy for resolving underspecification. We present \textsc{PleaSQLarify}, which operationalizes pragmatic repair by structuring interaction around interpretable decision variables that enable efficient clarification. A visual interface complements this by surfacing the action space for exploration, requesting user disambiguation, and making belief updates traceable across turns. In a study with twelve participants, \textsc{PleaSQLarify} helped users recognize alternative interpretations and efficiently resolve ...