Handling ambiguity and underspecification is an important challenge in natural language interfaces, particularly for tasks like text-to-SQL semantic parsing. We propose a modular approach that resolves ambiguity using natural language interpretations before mapping these to logical forms (e.g., SQL queries). Although LLMs excel at parsing unambiguous utterances, they show strong biases for ambiguous ones, typically predicting only preferred interpretations. We constructively exploit this bias to generate an initial set of preferred disambiguations and then apply a specialized infilling model to identify and generate missing interpretations. To train the infilling model, we introduce an annotation method that uses SQL execution to validate different meanings. Our approach improves interpretation coverage and generalizes across datasets with different annotation styles, database structures, and ambiguity types.

翻译：处理歧义和欠规范是自然语言接口中的一个重要挑战，尤其在文本到SQL语义解析等任务中。我们提出了一种模块化方法，该方法在将自然语言映射到逻辑形式（例如SQL查询）之前，先利用自然语言解释来消解歧义。尽管大型语言模型在解析无歧义话语方面表现出色，但对于有歧义的话语，它们表现出强烈的偏好偏差，通常只预测首选解释。我们建设性地利用这种偏差来生成一组初始的首选消歧结果，然后应用一个专门的填充模型来识别并生成缺失的解释。为了训练该填充模型，我们引入了一种利用SQL执行来验证不同含义的标注方法。我们的方法提高了解释的覆盖范围，并能泛化到具有不同标注风格、数据库结构和歧义类型的数据集上。