DFlow：基于大型语言模型的多样化对话流模拟
DFlow: Diverse Dialogue Flow Simulation with Large Language Models
AI总结：
本文提出DFlow方法，利用LLMs生成决策树结构任务计划以推导多样化对话轨迹，构建了含3,886个对话流的任务导向数据集，微调模型在下一动作预测任务上超越GPT-4。
摘要：
开发基于语言模型的对话智能体需要有效的数据来训练能够遵循特定任务逻辑的模型。然而，大多数现有的数据模拟方法侧重于增加话语层面在语言、主题或对话行为上的多样性，很大程度上忽略了对话层面任务逻辑多样性这一关键方面。本文提出一种新颖的数据模拟方法，旨在通过聚焦任务执行逻辑来增强合成对话的多样性。该方法利用LLMs生成决策树结构的任务计划，从而能够为给定任务推导出多样化的对话轨迹。每条轨迹被称为一个“对话流”，它指导着遵循独特轨迹的多轮对话的生成。我们将该方法应用于生成一个面向任务的对话数据集，包含跨15个不同领域的3,886个对话流。我们使用下一动作预测任务验证了该数据集的有效性，其中在我们的数据集上微调的模型优于包括GPT-4在内的强大基线。本文被接收后，我们计划公开发布代码和数据。
英文摘要：
Developing language model-based dialogue agents requires effective data to train models that can follow specific task logic. However, most existing data simulation methods focus on increasing diversity in language, topics, or dialogue acts at the utterance level, largely neglecting a critical aspect of task logic diversity at the dialogue level. This paper proposes a novel data simulation method designed to enhance the diversity of synthetic dialogues by focusing on task execution logic. Our method uses LLMs to generate decision tree-structured task plans, which enables the derivation of diverse dialogue trajectories for a given task. Each trajectory, referred to as a "dialog flow", guides the generation of a multi-turn dialogue that follows a unique trajectory. We apply this method to generate a task-oriented dialogue dataset comprising 3,886 dialogue flows across 15 different domains. We validate the effectiveness of this dataset using the next action prediction task, where models fine-tuned on our dataset outperform strong baselines, including GPT-4. Upon acceptance of this paper, we plan to release the code and data publicly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
