Building dialogue systems requires a large corpus of annotated dialogues. Such datasets are usually created via crowdsourcing, which is expensive and time-consuming. In this paper, we propose \textsc{Dialogic}, a novel dialogue simulation method based on large language model in-context learning to automate dataset creation. Seeded with a few annotated dialogues, \textsc{Dialogic} automatically selects in-context examples for demonstration and prompts GPT-3 to generate new dialogues and annotations in a controllable way. Our method can rapidly expand a small set of dialogue data with minimum or zero \textit{human involvement} and \textit{parameter update} and is thus much more cost-efficient and time-saving than crowdsourcing. Experimental results on the MultiWOZ dataset demonstrate that training a model on the simulated dialogues leads to even better performance than using the same amount of human-generated dialogues under the challenging low-resource settings, with as few as 85 dialogues as a seed. When enough data is available, our method can still serve as an effective data augmentation method. Human evaluation results also show that our simulated dialogues have near-human fluency and annotation accuracy. The code and data are available at \textbf{\url{https://github.com/Leezekun/dialogic}}.

翻译：构建对话系统需要大量标注对话语料。此类数据集通常通过众包创建，但这一过程既昂贵又耗时。本文提出\textsc{Dialogic}——一种基于大语言模型上下文学习的新型对话模拟方法，用于自动化数据集创建。以少量标注对话为种子，\textsc{Dialogic}自动选择上下文示例作为演示，并引导GPT-3以可控方式生成新对话及其标注。我们的方法能在极小或零\textit{人工干预}与\textit{参数更新}的情况下快速扩展少量对话数据，因此其成本效益与时效性远超众包。在MultiWOZ数据集上的实验表明：在资源稀缺的挑战性场景下（种子对话仅85条），基于模拟对话训练的模型性能甚至优于同等规模人工标注数据训练的模型。当数据量充足时，本方法仍可作为有效的数据增强手段。人工评估结果还显示，我们的模拟对话在流畅度与标注准确率上接近人类水平。代码与数据已发布于\textbf{\url{https://github.com/Leezekun/dialogic}}。