Goal-oriented chatbots are essential for automating user tasks, such as booking flights or making restaurant reservations. A key component of these systems is Dialogue State Tracking (DST), which interprets user intent and maintains the dialogue state. However, existing DST methods often rely on fixed ontologies and manually compiled slot values, limiting their adaptability to open-domain dialogues. We propose a novel approach that leverages instruction tuning and advanced prompt strategies to enhance DST performance, without relying on any predefined ontologies. Our method enables Large Language Model (LLM) to infer dialogue states through carefully designed prompts and includes an anti-hallucination mechanism to ensure accurate tracking in diverse conversation contexts. Additionally, we employ a Variational Graph Auto-Encoder (VGAE) to model and predict subsequent user intent. Our approach achieved state-of-the-art with a JGA of 42.57% outperforming existing ontology-less DST models, and performed well in open-domain real-world conversations. This work presents a significant advancement in creating more adaptive and accurate goal-oriented chatbots.

本研究解决了现有对话状态跟踪方法依赖固定本体和手动编写槽值限制其灵活性的问题。我们提出了一种新颖的方法，通过指令微调和高级提示策略提升对话状态跟踪性能，无需预定义本体，且采用变分图自编码器建模用户意图。此方法在开放领域的对话中表现出色，达到了42.57%的JGA，显著推动了目标导向聊天机器人的发展。

超越本体的目标导向对话状态跟踪