Abstract
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 without supervision using its inherent SQL programming capabilities combined with dialogue theory 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 dialogue theory. 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 to increase LLM explainability.
Abstract (translated)
大型语言模型（LLM）被广泛用作通用知识来源，但它们依赖于参数化知识，这限制了其可解释性和可信度。在任务导向对话（TOD）系统中，这种分离是明确的，使用一个由显式本体结构化的外部数据库来确保可解释性和可控性。然而，构建这样的本体需要手动标签或监督训练。 我们介绍了TeQoDO：一种利用LLM自身的SQL编程能力以及提示中的对话理论自主构建TOD本体的方法。这种方法不需要任何监督，并且从头开始创建本体。实验表明，TeQoDO在转移学习方法中表现更优，其构建的本体在下游对话状态跟踪任务上也具有竞争力。消融研究证明了对话理论的关键作用。此外，TeQoDO能够扩展以构建更大规模的本体，并且我们在维基百科和ArXiv数据集上对此进行了调查。 我们认为这是向更广泛地应用本体来增强LLM可解释性的一步。
URL
https://arxiv.org/abs/2507.23358
