@inproceedings{wang-etal-2025-aelc,
    title = "{AELC}: Adaptive Entity Linking with {LLM}-Driven Contextualization",
    author = "Wang, Fang  and
      Tao, Zhengwei  and
      Wang, Ming  and
      Hu, Minghao  and
      Bai, Xiaoying",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.231/",
    doi = "10.18653/v1/2025.findings-emnlp.231",
    pages = "4313--4327",
    ISBN = "979-8-89176-335-7",
    abstract = "Entity linking (EL) focuses on accurately associating ambiguous mentions in text with corresponding entities in a knowledge graph. Traditional methods mainly rely on fine-tuning or training on specific datasets. However, they suffer from insufficient semantic comprehension, high training costs, and poor scalability. Large Language Models (LLMs) offer promising solutions for EL, but face key challenges: weak simple-prompt performance, costly fine-tuning, and limited recall and precision due to the lack of LLMs use in candidate generation. Building on this, we introduce a novel framework: **A**daptive **E**ntity **L**inking with LLM-Driven **C**ontextualization. AELC, for the first time, introduces the combination of high-density key information condensation prompt and tool-invocation strategy, using a unified format semantic filtering strategy and an adaptive iterative retrieval mechanism to dynamically optimize the candidate set, significantly enhancing both precision and coverage. Furthermore, we innovatively reformulate the EL task as a multiple-choice problem, enabling multi-round reasoning to substantially improve the model{'}s discriminative capability and robustness. Experiments on four public benchmark datasets demonstrate that AELC achieves state-of-the-art performance. Further ablation studies validate the effectiveness of each module."
}