Collecting high quality conversational data can be very expensive for most applications and infeasible for others due to privacy, ethical, or similar concerns. A promising direction to tackle this problem is to generate synthetic dialogues by prompting large language models. In this work, we use a small set of expert-written conversations as in-context examples to synthesize a social conversation dataset using prompting. We perform several thorough evaluations of our synthetic conversations compared to human-collected conversations. This includes various dimensions of conversation quality with human evaluation directly on the synthesized conversations, and interactive human evaluation of chatbots fine-tuned on the synthetically generated dataset. We additionally demonstrate that this prompting approach is generalizable to multi-party conversations, providing potential to create new synthetic data for multi-party tasks. Our synthetic multi-party conversations were rated more favorably across all measured dimensions compared to conversation excerpts sampled from a human-collected multi-party dataset.

翻译：收集高质量对话数据对于大多数应用而言成本高昂，而由于隐私、伦理或其他类似原因，对其他应用而言则不可行。解决这一问题的一个有前景的方向是通过提示大型语言模型生成合成对话。在本工作中，我们利用少量专家撰写的对话作为上下文示例，通过提示方法合成一个社交对话数据集。我们对我们合成的对话与人工收集的对话进行了多次全面评估，包括直接通过人工评估对话质量的多个维度，以及在合成数据集上微调的聊天机器人的交互式人工评估。此外，我们证明这种提示方法可推广至多方对话，为创建新的多方任务合成数据提供了潜力。与从人工收集的多方数据集中抽取的对话片段相比，我们合成的多方对话在所有评估维度上均获得了更有利的评价。