Entity linking (mapping ambiguous mentions in text to entities in a knowledge base) is a foundational step in tasks such as knowledge graph construction, question-answering, and information extraction. Our method, LELA, is a modular coarse-to-fine approach that leverages the capabilities of large language models (LLMs), and works with different target domains, knowledge bases and LLMs, without any fine-tuning phase. Our experiments across various entity linking settings show that LELA is highly competitive with fine-tuned approaches, and substantially outperforms the non-fine-tuned ones.
@article{arxiv.2601.05192,
title = {LELA: an LLM-based Entity Linking Approach with Zero-Shot Domain Adaptation},
author = {Samy Haffoudhi and Fabian M. Suchanek and Nils Holzenberger},
journal= {arXiv preprint arXiv:2601.05192},
year = {2026}
}