@inproceedings{asai-etal-2022-evidentiality,
    title = "Evidentiality-guided Generation for Knowledge-Intensive {NLP} Tasks",
    author = "Asai, Akari  and
      Gardner, Matt  and
      Hajishirzi, Hannaneh",
    editor = "Carpuat, Marine  and
      de Marneffe, Marie-Catherine  and
      Meza Ruiz, Ivan Vladimir",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/2022.naacl-main.162/",
    doi = "10.18653/v1/2022.naacl-main.162",
    pages = "2226--2243",
    abstract = "Retrieval-augmented generation models have shown state-of-the-art performance across many knowledge-intensive NLP tasks such as open-domain question answering and fact verification. These models are trained to generate a final output given retrieved passages that can be irrelevant to an input query, leading to learning spurious cues or memorization. This work introduces a method to incorporate \textit{evidentiality} of passages{---}whether a passage contains correct evidence to support the output{---}into training the generator. We introduce a multi-task learning framework to jointly generate the final output and predict the \textit{evidentiality} of each passage. Furthermore, we introduce a new task-agnostic method for obtaining high-quality \textit{silver} evidentiality labels, addressing the issues of gold evidentiality labels being unavailable in most domains. Our experiments on five datasets across three knowledge-intensive tasks show that our new evidentiality-guided generator significantly outperforms its direct counterpart on all of them, and advances the state of the art on three of them. Our analysis shows that multi-task learning and silver evidentiality mining play key roles. Our code is available at \url{https://github.com/AkariAsai/evidentiality_qa}"
}