@inproceedings{sun-etal-2026-knowledge,
    title = "Knowledge Extraction on Semi-Structured Content: Does It Remain Relevant for Question Answering in the Era of {LLM}s?",
    author = "Sun, Kai  and
      Huang, Yin  and
      Mehra, Srishti  and
      Kachuee, Mohammad  and
      Chen, Xilun  and
      Tao, Renjie  and
      Lin, Zhaojiang  and
      Jessee, Andrea  and
      Shah, Nirav  and
      Betty, Alex L  and
      Liu, Yue  and
      Kumar, Anuj  and
      Yih, Wen-tau  and
      Dong, Xin Luna",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.91/",
    pages = "2055--2074",
    ISBN = "979-8-89176-380-7",
    abstract = "The advent of Large Language Models (LLMs) has significantly advanced web-based Question Answering (QA) systems over semi-structured content, raising questions about the continued utility of knowledge extraction for question answering. This paper investigates the value of triple extraction in this new paradigm by extending an existing benchmark with knowledge extraction annotations and evaluating commercial and open-source LLMs of varying sizes. Our results show that web-scale knowledge extraction remains a challenging task for LLMs. Despite achieving high QA accuracy, LLMs can still benefit from knowledge extraction, through augmentation with extracted triples and multi-task learning. These findings provide insights into the evolving role of knowledge triple extraction in web-based QA and highlight strategies for maximizing LLM effectiveness across different model sizes and resource settings."
}