用图形化结构生成任务型对话，降低数据构建门槛。
Leveraging Graph Structures and Large Language Models for End-to-End Synthetic Task-Oriented Dialogues
- 通过JSON定义对话流程图，无需编程即可生成对话
- 在多个领域生成高质量对话，效果接近人工标注数据
- 适合非技术用户快速构建任务型对话数据集
训练任务型对话系统成本高、耗时长，主要因为需要涵盖多种意图的高质量数据集。传统方法依赖大量人工标注，而近期方法利用大语言模型（LLMs）生成合成数据。但这些方法常需定制提示词或代码，限制了非技术用户的使用。我们提出GraphTOD，一种端到端框架，用户只需以JSON格式指定转换图，即可生成任务型对话。评估表明，GraphTOD在多个领域均能生成高质量对话，显著降低了数据集创建的成本与复杂度。
原文摘要 · Abstract (English)
Training task-oriented dialogue systems is both costly and time-consuming, due to the need for high-quality datasets encompassing diverse intents. Traditional methods depend on extensive human annotation, while recent advancements leverage large language models (LLMs) to generate synthetic data. However, these approaches often require custom prompts or code, limiting accessibility for non-technical users. We introduce GraphTOD, an end-to-end framework that simplifies the generation of task-oriented dialogues. Users can create dialogues by specifying transition graphs in JSON format. Our evaluation demonstrates that GraphTOD generates high-quality dialogues across various domains, significantly lowering the cost and complexity of dataset creation.
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