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Autores principales: Haffoudhi, Samy, Suchanek, Fabian M., Holzenberger, Nils
Formato: Preprint
Publicado: 2026
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Acceso en línea:https://arxiv.org/abs/2601.05192
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author Haffoudhi, Samy
Suchanek, Fabian M.
Holzenberger, Nils
author_facet Haffoudhi, Samy
Suchanek, Fabian M.
Holzenberger, Nils
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2601_05192
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle LELA: an LLM-based Entity Linking Approach with Zero-Shot Domain Adaptation
Haffoudhi, Samy
Suchanek, Fabian M.
Holzenberger, Nils
Computation and Language
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.
title LELA: an LLM-based Entity Linking Approach with Zero-Shot Domain Adaptation
topic Computation and Language
url https://arxiv.org/abs/2601.05192