@inproceedings{qin-etal-2023-webcpm,
    title = "{W}eb{CPM}: Interactive Web Search for {C}hinese Long-form Question Answering",
    author = "Qin, Yujia  and
      Cai, Zihan  and
      Jin, Dian  and
      Yan, Lan  and
      Liang, Shihao  and
      Zhu, Kunlun  and
      Lin, Yankai  and
      Han, Xu  and
      Ding, Ning  and
      Wang, Huadong  and
      Xie, Ruobing  and
      Qi, Fanchao  and
      Liu, Zhiyuan  and
      Sun, Maosong  and
      Zhou, Jie",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.499",
    doi = "10.18653/v1/2023.acl-long.499",
    pages = "8968--8988",
    abstract = "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 15,372 supporting facts and 125,954 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. The interface, dataset, and codes are publicly available at \url{https://github.com/thunlp/WebCPM}.",
}

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 15,372 supporting facts and 125,954 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. The interface, dataset, and codes are publicly available at \url{https://github.com/thunlp/WebCPM}.
Qin, Yujia Cai, Zihan Jin, Dian Yan, Lan Liang, Shihao Zhu, Kunlun Lin, Yankai Han, Xu Ding, Ning Wang, Huadong Xie, Ruobing Qi, Fanchao Liu, Zhiyuan Sun, Maosong Zhou, Jie
WebCPM: Interactive Web Search for Chinese Long-form Question Answering