Long-form question answering (LFQA) aims at answering complex, open-ended questions with detailed, paragraph-length responses. The de facto paradigm of LFQA necessitates two procedures: information retrieval, which searches for relevant supporting facts, and information synthesis, which integrates these facts into a coherent answer. In this paper, we introduce WebCPM, the first Chinese LFQA dataset. One unique feature of WebCPM is that its information retrieval is based on interactive web search, which engages with a search engine in real time. Following WebGPT, we develop a web search interface. We recruit annotators to search for relevant information using our interface and then answer questions. Meanwhile, the web search behaviors of our annotators would be recorded. In total, we collect 5,500 high-quality question-answer pairs, together with 14,315 supporting facts and 121,330 web search actions. We fine-tune pre-trained language models to imitate human behaviors for web search and to generate answers based on the collected facts. Our LFQA pipeline, built on these fine-tuned models, generates answers that are no worse than human-written ones in 32.5% and 47.5% of the cases on our dataset and DuReader, respectively.

翻译：长文本问答旨在通过详细的段落级回答来解答复杂、开放性的问题。当前长文本问答的典型范式包含两个步骤：信息检索（搜索相关支撑事实）与信息整合（将这些事实融合成连贯回答）。本文提出首个中文长文本问答数据集WebCPM。该数据集的一个独特之处在于其基于交互式网络搜索的信息检索方式——实时与搜索引擎进行交互。遵循WebGPT的思路，我们开发了网络搜索接口。我们招募标注员通过该接口搜索相关信息并回答问题，同时记录其网络搜索行为。最终收集了5,500个高质量问答对，包含14,315个支撑事实和121,330次网络搜索动作。我们微调预训练语言模型，使其模仿人类网络搜索行为，并基于收集的事实生成答案。基于这些微调模型构建的长文本问答流水线，在我们数据集和DuReader上分别有32.5%和47.5%的案例中生成的答案不逊于人工撰写的答案。