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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2601.05192 |
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| _version_ | 1866908754086199296 |
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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 |