| Title: | Zero-shot evaluation of ChatGPT for food named-entity recognition and linking |
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| Authors: | ID Ogrinc, Matevž, Institut Jožef Stefan (Author)
ID Koroušić-Seljak, Barbara, Institut Jožef Stefan (Author)
ID Eftimov, Tome, Institut Jožef Stefan (Author) |
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| Files: |  URL - Source URL, visit https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2024.1429259/full
 PDF - Presentation file, download (1,08 MB)
MD5: D881B2E5F4712654B8E1775E2CE74E99
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| Language: | English |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: |  IJS - Jožef Stefan Institute
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| Abstract: | 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. |
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| Keywords: | ChatGPT, food data, named-entity recognition, named-entity linking |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 07.05.2024 |
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| Article acceptance date: | 26.07.2024 |
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| Publication date: | 13.08.2024 |
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| Publisher: | Frontiers |
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| Year of publishing: | 2024 |
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| Number of pages: | str. 1-10 |
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| Numbering: | Vol. 11 |
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| Source: | Švica |
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| PID: | 20.500.12556/DiRROS-20436  |
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| UDC: | 004.8 |
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| ISSN on article: | 2296-861X |
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| DOI: | 10.3389/fnut.2024.1429259 |
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| COBISS.SI-ID: | 206958851 |
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| Copyright: | © 2024 Ogrinc, Korousˇic´, Seljak and Eftimov. |
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| Note: | Nasl. z nasl. zaslona;
Opis vira z dne 10. 9. 2024;
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| Publication date in DiRROS: | 16.09.2024 |
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| Views: | 216 |
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| Downloads: | 95 |
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