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 ambiguity. Our findings highlight pragmatic repair as a design principle that fosters effective user control in natural language interfaces.

翻译：自然语言数据库接口拓宽了数据访问途径，但在输入存在歧义时仍显脆弱。标准方法通常将不确定性压缩为单一查询，对用户意图与系统解读之间的不匹配缺乏有效支持。我们通过语用推理重新审视这一挑战：用户倾向于简化表达，而系统则基于可能偏离用户意图的动作空间先验进行操作。在此视角下，语用修复——通过最小化交互实现渐进式澄清——成为解决欠规范问题的自然策略。本文提出 \textsc{PleaSQLarify}，该方法通过围绕可解释决策变量构建交互来实现语用修复，从而实现高效澄清。可视化界面通过呈现可供探索的动作空间、请求用户消歧以及使信念更新在对话轮次间可追溯，对此形成有效补充。在包含十二名参与者的研究中，\textsc{PleaSQLarify} 帮助用户识别替代解读并有效消解歧义。我们的研究结果突显了语用修复作为设计原则在自然语言界面中促进用户有效控制的重要价值。