@inproceedings{zhu-etal-2025-ger,
    title = "{GER}-{LLM}: Efficient and Effective Geospatial Entity Resolution with Large Language Model",
    author = "Zhu, Haojia  and
      Li, Zhicheng  and
      Jin, Jiahui",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.emnlp-main.1186/",
    doi = "10.18653/v1/2025.emnlp-main.1186",
    pages = "23272--23288",
    ISBN = "979-8-89176-332-6",
    abstract = "Geospatial Entity Resolution (GER) plays a central role in integrating spatial data from diverse sources. However, existing methods are limited by their reliance on large amounts of training data and their inability to incorporate commonsense knowledge. While recent advances in Large Language Models (LLMs) offer strong semantic reasoning and zero-shot capabilities, directly applying them to GER remains inadequate due to their limited spatial understanding and high inference cost. In this work, we present GER-LLM, a framework that integrates LLMs into the GER pipeline. To address the challenge of spatial understanding, we design a spatially informed blocking strategy based on adaptive quadtree partitioning and Area of Interest (AOI) detection, preserving both spatial proximity and functional relationships. To mitigate inference overhead, we introduce a group prompting mechanism with graph-based conflict resolution, enabling joint evaluation of diverse candidate pairs and enforcing global consistency across alignment decisions. Extensive experiments on real-world datasets demonstrate the effectiveness of our approach, yielding significant improvements over state-of-the-art methods."
}