基于思维链的检索增强生成实证研究
An Empirical Study of Retrieval Augmented Generation with Chain-of-Thought
AI总结：
本文实证研究了RAFT方法在生成式对话模型中的应用，通过结合思维链、监督微调和检索增强生成，在多种推理任务上验证了其提升信息提取与逻辑推理能力的有效性。
摘要：
自2022年底ChatGPT发布以来，以ChatGPT为代表的生成式对话模型迅速成为日常生活中的重要工具。随着用户期望不断提高，增强生成式对话模型解决复杂问题的能力已成为当前研究的焦点。本文深入探讨了RAFT（Retrieval Augmented Fine-Tuning，检索增强微调）方法在提升生成式对话模型性能方面的有效性。RAFT将思维链与模型监督微调（SFT）和检索增强生成（RAG）相结合，显著增强了模型的信息提取和逻辑推理能力。我们在多个数据集上评估了RAFT方法，并分析了其在各种推理任务中的表现，包括长文本问答和短文本问答任务、中英文任务以及支持性推理和比较推理任务。值得注意的是，该研究填补了以往研究在长文本问答任务和中文数据集方面的空白。此外，我们还评估了思维链（CoT）在RAFT方法中的益处。这项工作为专注于提升生成式对话模型性能的研究提供了有价值的见解。
英文摘要：
Since the launch of ChatGPT at the end of 2022, generative dialogue models represented by ChatGPT have quickly become essential tools in daily life. As user expectations increase, enhancing the capability of generative dialogue models to solve complex problems has become a focal point of current research. This paper delves into the effectiveness of the RAFT (Retrieval Augmented Fine-Tuning) method in improving the performance of Generative dialogue models. RAFT combines chain-of-thought with model supervised fine-tuning (SFT) and retrieval augmented generation (RAG), which significantly enhanced the model's information extraction and logical reasoning abilities. We evaluated the RAFT method across multiple datasets and analysed its performance in various reasoning tasks, including long-form QA and short-form QA tasks, tasks in both Chinese and English, and supportive and comparison reasoning tasks. Notably, it addresses the gaps in previous research regarding long-form QA tasks and Chinese datasets. Moreover, we also evaluate the benefit of the chain-of-thought (CoT) in the RAFT method. This work offers valuable insights for studies focused on enhancing the performance of generative dialogue models.
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。
