@inproceedings{efeoglu-paschke-2025-fine,
    title = "Fine-Tuning Large Language Models for Relation Extraction within a Retrieval-Augmented Generation Framework",
    author = "Efeoglu, Sefika  and
      Paschke, Adrian",
    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-1.1/",
    pages = "1--7",
    ISBN = "979-8-89176-286-2",
    abstract = "Information Extraction (IE) plays a pivotal role in transforming unstructured data into structured formats, such as Knowledge Graphs. One of the main tasks within IE is Relation Extraction (RE), which identifies relations between entities in text data. This process enriches the semantic understanding of documents, enabling more precise information retrieval and query answering. Recent works leveraging pre-trained language models have demonstrated significant performance improvements in RE. In the current era of Large Language Models (LLMs), fine-tuning these LLMs can mitigate the limitations of zero-shot RE methods, particularly in overcoming the domain adaptation challenges inherent in RE. This work explores not only the effectiveness of fine-tuned LLMs but also their integration into a Retrieval-Augmented Generation (RAG)-based RE approach to address domain adaptation challenges when general-purpose LLMs serve as generators within the RAG framework. Empirical evaluations on the TACRED, TACRED-Revisited (TACREV), and Re-TACRED datasets reveal substantial performance improvements with fine-tuned LLMs, such as Llama2-7B, Mistral-7B, and Flan-T5 Large and surpass previous methods on these datasets."
}