20.500.12556/DiRROS-20436 Zero-shot evaluation of ChatGPT for food named-entity recognition and linking Introduction: Recognizing and extracting key information from textual data plays an important role in intelligent systems by maintaining up-to-date knowledge, reinforcing informed decision-making, question-answering, and more. It is especially apparent in the food domain, where critical information guides the decisions of nutritionists and clinicians. The information extraction process involves two natural language processing tasks named entity recognition—NER and named entity linking—NEL. With the emergence of large language models (LLMs), especially ChatGPT, many areas began incorporating its knowledge to reduce workloads or simplify tasks. In the field of food, however, we noticed an opportunity to involve ChatGPT in NER and NEL. Methods: To assess ChatGPT's capabilities, we have evaluated its two versions, ChatGPT-3.5 and ChatGPT-4, focusing on their performance across both NER and NEL tasks, emphasizing food-related data. To benchmark our results in the food domain, we also investigated its capabilities in a more broadly investigated biomedical domain. By evaluating its zero-shot capabilities, we were able to ascertain the strengths and weaknesses of the two versions of ChatGPT. Results: Despite being able to show promising results in NER compared to other models. When tasked with linking entities to their identifiers from semantic models ChatGPT's effectiveness falls drastically. Discussion: While the integration of ChatGPT holds potential across various fields, it is crucial to approach its use with caution, particularly in relying on its responses for critical decisions in food and bio-medicine. ChatGPT food data named-entity recognition named-entity linking ChatGPT prehranski podatki true false true Frontiers Švica Angleški jezik Slovenski jezik © 2024 Ogrinc, Korousˇic´, Seljak and Eftimov. Neznano 2024-09-16 12:55:55 2024-09-16 12:55:55 2024-09-17 04:02:08 0000-00-00 00:00:00 2024 0 0 Nasl. z nasl. zaslona; Opis vira z dne 10. 9. 2024; str. 1-10 Vol. 11 13 Aug. 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2024-05-07 2024-07-26 2024-08-13 004.8 2296-861X 10.3389/fnut.2024.1429259 206958851 https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2024.1429259/full 1 7c5726bc-7409-11ef-9d3a-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=28776 RAZ_Ogrinc_Matevz_2024.pdf RAZ_Ogrinc_Matevz_2024.pdf 1 D881B2E5F4712654B8E1775E2CE74E99 893bcd1b9b685e4811c05779d99fadcb662effc1601a01806bfaaf0e6b9bdc08 c70c8877-740a-11ef-9d3a-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=28779 Institut Jožef Stefan 0 0 0