@inproceedings{popovic-etal-2025-docie,
    title = "{D}oc{IE}@{XLLM}25: In-Context Learning for Information Extraction using Fully Synthetic Demonstrations",
    author = {Popovic, Nicholas  and
      Kangen, Ashish  and
      Schopf, Tim  and
      F{\"a}rber, Michael},
    editor = "Fei, Hao  and
      Tu, Kewei  and
      Zhang, Yuhui  and
      Hu, Xiang  and
      Han, Wenjuan  and
      Jia, Zixia  and
      Zheng, Zilong  and
      Cao, Yixin  and
      Zhang, Meishan  and
      Lu, Wei  and
      Siddharth, N.  and
      {\O}vrelid, Lilja  and
      Xue, Nianwen  and
      Zhang, Yue",
    booktitle = "Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.xllm-1.26/",
    pages = "298--309",
    ISBN = "979-8-89176-286-2",
    abstract = "Large, high-quality annotated corpora remain scarce in document-level entity and relation extraction in zero-shot or few-shot settings.In this paper, we present a fully automatic, LLM-based pipeline for synthetic data generation and in-context learning for document-level entity and relation extraction.In contrast to existing approaches that rely on manually annotated demonstrations or direct zero-shot inference, our method combines synthetic data generation with retrieval-based in-context learning, using a reasoning-optimized language model.This allows us to build a high-quality demonstration database without manual annotation and to dynamically retrieve relevant examples at inference time.Based on our approach we produce a synthetic dataset of over $5k$ Wikipedia abstracts with approximately $59k$ entities and $30k$ relation triples.Finally, we evaluate in-context learning performance on the DocIE shared task, extracting entities and relations from long documents in a zero-shot setting.The code and synthetic dataset are made available for future research."
}