We introduce a technique for multi-document grounded multi-turn synthetic dialog generation that incorporates three main ideas. First, we control the overall dialog flow using taxonomy-driven user queries that are generated with Chain-of-Thought (CoT) prompting. Second, we support the generation of multi-document grounded dialogs by mimicking real-world use of retrievers to update the grounding documents after every user-turn in the dialog. Third, we apply LLM-as-a-Judge to filter out queries with incorrect answers. Human evaluation of the synthetic dialog data suggests that the data is diverse, coherent, and includes mostly correct answers. Both human and automatic evaluations of answerable queries indicate that models fine-tuned on synthetic dialogs consistently out-perform those fine-tuned on existing human generated training data across four publicly available multi-turn document grounded benchmark test sets.

本研究解决了多文档生成的多轮对话中的控制与准确性问题。通过使用以分类法驱动的用户查询和实时更新文档的模拟方法，提出了一种创新的对话生成技术。结果表明，合成的对话数据在多样性、一致性和准确性方面均优于传统人类生成的数据，具有显著的应用潜力。

多文档基础的多轮合成对话生成