FoodSEM: Large Language Model Specialized in Food Named-Entity Linking
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908560898654208 |
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| author | Gjorgjevikj, Ana Martinc, Matej Cenikj, Gjorgjina Džeroski, Sašo Seljak, Barbara Koroušić Eftimov, Tome |
| author_facet | Gjorgjevikj, Ana Martinc, Matej Cenikj, Gjorgjina Džeroski, Sašo Seljak, Barbara Koroušić Eftimov, Tome |
| contents | This paper introduces FoodSEM, a state-of-the-art fine-tuned open-source large language model (LLM) for named-entity linking (NEL) to food-related ontologies. To the best of our knowledge, food NEL is a task that cannot be accurately solved by state-of-the-art general-purpose (large) language models or custom domain-specific models/systems. Through an instruction-response (IR) scenario, FoodSEM links food-related entities mentioned in a text to several ontologies, including FoodOn, SNOMED-CT, and the Hansard taxonomy. The FoodSEM model achieves state-of-the-art performance compared to related models/systems, with F1 scores even reaching 98% on some ontologies and datasets. The presented comparative analyses against zero-shot, one-shot, and few-shot LLM prompting baselines further highlight FoodSEM's superior performance over its non-fine-tuned version. By making FoodSEM and its related resources publicly available, the main contributions of this article include (1) publishing a food-annotated corpora into an IR format suitable for LLM fine-tuning/evaluation, (2) publishing a robust model to advance the semantic understanding of text in the food domain, and (3) providing a strong baseline on food NEL for future benchmarking. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22125 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FoodSEM: Large Language Model Specialized in Food Named-Entity Linking Gjorgjevikj, Ana Martinc, Matej Cenikj, Gjorgjina Džeroski, Sašo Seljak, Barbara Koroušić Eftimov, Tome Computation and Language Information Retrieval This paper introduces FoodSEM, a state-of-the-art fine-tuned open-source large language model (LLM) for named-entity linking (NEL) to food-related ontologies. To the best of our knowledge, food NEL is a task that cannot be accurately solved by state-of-the-art general-purpose (large) language models or custom domain-specific models/systems. Through an instruction-response (IR) scenario, FoodSEM links food-related entities mentioned in a text to several ontologies, including FoodOn, SNOMED-CT, and the Hansard taxonomy. The FoodSEM model achieves state-of-the-art performance compared to related models/systems, with F1 scores even reaching 98% on some ontologies and datasets. The presented comparative analyses against zero-shot, one-shot, and few-shot LLM prompting baselines further highlight FoodSEM's superior performance over its non-fine-tuned version. By making FoodSEM and its related resources publicly available, the main contributions of this article include (1) publishing a food-annotated corpora into an IR format suitable for LLM fine-tuning/evaluation, (2) publishing a robust model to advance the semantic understanding of text in the food domain, and (3) providing a strong baseline on food NEL for future benchmarking. |
| title | FoodSEM: Large Language Model Specialized in Food Named-Entity Linking |
| topic | Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2509.22125 |