  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.
