无需预设语义库，用提示词让大模型自动理解对话状态。
Beyond Ontology in Dialogue State Tracking for Goal-Oriented Chatbot
- 用指令微调和精心设计的提示词驱动大模型推理对话状态。
- 在无语义库条件下达到42.57%的JGA，优于现有方法。
- 适合需要灵活应对开放域对话的智能客服系统研发者。
面向目标的聊天机器人对自动化用户任务（如订票、订餐）至关重要。对话状态追踪（DST）是其核心组件，用于理解用户意图并维护对话状态。然而，现有DST方法通常依赖固定语义库和人工编写的槽位值，难以适应开放域对话。本文提出一种新方法，通过指令微调和先进提示策略提升DST性能，无需任何预定义语义库。该方法使大语言模型（LLM）通过精心设计的提示推断对话状态，并引入防幻觉机制以确保在多样对话场景中的准确性。此外，采用变分图自编码器（VGAE）建模并预测后续用户意图。实验表明，该方法在无语义库条件下达到42.57%的联合准确率（JGA），优于现有同类模型，在真实开放域对话中表现优异。本工作显著推进了更自适应、更精准的目标导向聊天机器人的发展。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。