用大模型生成多样对话流程，提升任务型对话数据质量。
DFlow: Diverse Dialogue Flow Simulation with Large Language Models
- 用大模型构建决策树任务计划，生成多样化对话轨迹。
- 创建涵盖15个领域的3886条对话流程，显著提升任务逻辑多样性。
- 适合需要高质量任务型对话数据的研究者与开发者使用。
构建基于大语言模型的对话代理需要有效的训练数据以使模型遵循特定任务逻辑。然而，现有数据生成方法多关注语句层面的语言、话题或对话行为的多样性，忽视了对话层面的任务逻辑多样性这一关键问题。本文提出一种新方法，通过大模型生成结构化的决策树任务计划，从而推导出多种任务执行路径（称为“对话流”），每条路径引导生成一条独特多轮对话。该方法生成了一个面向任务的对话数据集，包含15个不同领域共3886条对话流。在下一步动作预测任务中，微调该数据集的模型性能优于多个强基线，包括GPT-4。论文被接受后，将公开代码与数据。
原文摘要 · Abstract (English)
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.
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