面向文本到SQL的任务型对话本体构建 (Text-to-SQL Task-oriented Dialogue Ontology Construction)

Large language models (LLMs) are widely used as general-purpose knowledge sources, but they rely on parametric knowledge, limiting explainability and trustworthiness. In task-oriented dialogue (TOD) systems, this separation is explicit, using an external database structured by an explicit ontology to ensure explainability and controllability. However, building such ontologies requires manual labels or supervised training. We introduce TeQoDO: a Text-to-SQL task-oriented Dialogue Ontology construction method. Here, an LLM autonomously builds a TOD ontology from scratch using only its inherent SQL programming capabilities combined with concepts from modular TOD systems provided in the prompt. We show that TeQoDO outperforms transfer learning approaches, and its constructed ontology is competitive on a downstream dialogue state tracking task. Ablation studies demonstrate the key role of modular TOD system concepts. TeQoDO also scales to allow construction of much larger ontologies, which we investigate on a Wikipedia and arXiv dataset. We view this as a step towards broader application of ontologies.

翻译：大型语言模型（LLM）被广泛用作通用知识源，但它们依赖于参数化知识，限制了可解释性与可信度。在任务型对话（TOD）系统中，这种分离是显式的，系统通过使用由显式本体结构化的外部数据库来确保可解释性与可控性。然而，构建此类本体需要人工标注或有监督训练。我们提出了TeQoDO：一种面向文本到SQL的任务型对话本体构建方法。该方法中，一个LLM仅利用其固有的SQL编程能力，结合提示中提供的模块化TOD系统概念，即可从零开始自主构建TOD本体。我们证明，TeQoDO的性能优于迁移学习方法，并且其构建的本体在下游对话状态跟踪任务中具有竞争力。消融研究证明了模块化TOD系统概念的关键作用。TeQoDO还能扩展到允许构建更大的本体，我们在一个维基百科和arXiv数据集上对此进行了探究。我们认为这是朝着本体更广泛应用迈出的一步。