@inproceedings{swarup-etal-2025-syntax,
    title = "From Syntax to Semantics: Evaluating the Impact of Linguistic Structures on {LLM}-Based Information Extraction",
    author = "Swarup, Anushka  and
      Bhandarkar, Avanti  and
      Wilson, Ronald  and
      Pan, Tianyu  and
      Woodard, Damon",
    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-1.5/",
    pages = "36--48",
    ISBN = "979-8-89176-286-2",
    abstract = "Large Language Models (LLMs) have brought significant breakthroughs across all areas of Natural Language Processing (NLP), including Information Extraction (IE). However, knowledge gaps remain regarding their effectiveness in extracting entity-relation triplets, i.e. Joint Relation Extraction (JRE). JRE has been a key operation in creating knowledge bases that can be used to enhance Retrieval Augmented Generation (RAG) systems. Prior work highlights low-quality triplets generated by LLMs. Thus, this work investigates the impact of incorporating linguistic structures, such as constituency and dependency trees and semantic role labeling, to enhance the quality of the extracted triplets. The findings suggest that incorporating specific structural information enhances the uniqueness and topical relevance of the triplets, particularly in scenarios where multiple relationships are present."
}