LELA: An End-to-end LLM-based Entity Linking Framework with Zero-shot Domain Adaptation
The paper introduces LELA, an end-to-end framework for entity linking that utilizes large language models (LLMs) and supports zero-shot domain adaptation. This framework aims to enhance the applicability of entity linking in various real-world scenarios by providing a modular and domain-agnostic solution. Experimental results demonstrate LELA's effectiveness and robustness across different settings, and a demo is available for users to test the system with their own texts.
- ▪LELA is a modular and domain-agnostic entity disambiguation method.
- ▪The framework integrates zero-shot Named Entity Recognition to create a complete entity-linking pipeline.
- ▪Experimental results validate LELA's performance across diverse settings.
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Computer Science > Artificial Intelligence arXiv:2605.26956 (cs) [Submitted on 26 May 2026] Title:LELA: An End-to-end LLM-based Entity Linking Framework with Zero-shot Domain Adaptation Authors:Samy Haffoudhi (IP Paris, LTCI, DIG), Nikola Dobričić (IP Paris), Fabian Suchanek (IP Paris, LTCI), Nils Holzenberger View a PDF of the paper titled LELA: An End-to-end LLM-based Entity Linking Framework with Zero-shot Domain Adaptation, by Samy Haffoudhi (IP Paris and 6 other authors View PDF 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.
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