LELA: An End-to-end LLM-based Entity Linking Framework with Zero-shot Domain Adaptation

Samy Haffoudhi; Nikola Dobricic; Fabian M. Suchanek; Nils Holzenberger

IJCAI 20260 citations

LELA: An End-to-end LLM-based Entity Linking Framework with Zero-shot Domain Adaptation

Samy Haffoudhi, Nikola Dobricic, Fabian M. Suchanek, Nils Holzenberger

Abstract

Entity linking is a key component of many downstream NLP systems, yet existing approaches are often tied to the specific target knowledge bases and domains, limiting their real world application. In this paper, we extend LELA, a modular and domain-agnostic LLM-based entity disambiguation method, into a practical Python library that integrates zero-shot Named Entity Recognition (NER) -- thereby providing a complete end-to-end pipeline for entity-linking in real-world usage. We provide experimental results validating LELA's performance and robustness across diverse entity linking settings. In our demo, users can play with the system on their own input texts. All code is publicly available at https://github.com/NDobricic/LELA, and a video is at https://www.youtube.com/watch?v=WdupiRjLbR4.

BibTeX

@inproceedings{ijcai2026_lelaanendtoendll,
  title = {LELA: An End-to-end LLM-based Entity Linking Framework with Zero-shot Domain Adaptation},
  author = {Samy Haffoudhi and Nikola Dobricic and Fabian M. Suchanek and Nils Holzenberger},
  booktitle = {IJCAI 2026},
  year = {2026}
}