ChatGPT: not all languages are equal
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GPT-3.5 exhibits a decline in NLP proficiency in non-English languages relative to English.
“Additionally, GPT-3.5 exhibits a decline in NLP proficiency in non-English languages relative to English [20].”
From Performance of GPT-3.5 and GPT-4 on the Japanese Medical Licensing Examination: Comparison Study (2023) · cites this paper as [20]
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Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots
Gill, S. S., Xu, M.-X., Patros, P., Wu, H.-M., Kaur, R., Kaur, K., Fuller, S., Singh, M., Arora, P., Parlikad, A., et al. (2023) · Internet Things Cyber Phys. Syst. · doi:10.1016/j.iotcps.2023.06.002 · Cited by 466 · see citing works
“Despite the technology divide, ChatGPT has seen the greatest consumer growth since its introduction and currently supports more than 100 million active users [4].”
BackgroundGenerative AI in Medicine and Healthcare: Promises, Opportunities and Challenges
Zhang, P., & Kamel Boulos, M. N. (2023) · Future Internet · doi:10.3390/fi15090286 · Cited by 411 · see citing works
“Generative AI is also prone to various forms of bias depending on how it has been trained [41] and may not always perform equally well across different languages [56].”
BackgroundAcademic integrity and artificial intelligence: is ChatGPT hype, hero or heresy?
Currie, G. M. (2023) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2023.04.008 · Cited by 283 · see citing works
Performance of GPT-3.5 and GPT-4 on the Japanese Medical Licensing Examination: Comparison Study
Takagi, S., Watari, T., Erabi, A., & Sakaguchi, K. (2023) · JMIR Medical Education · doi:10.2196/48002 · Cited by 271 · see citing works
ChatGPT in education: Methods, potentials, and limitations
Memarian, B., & Doleck, T. (2023) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2023.100022 · Cited by 266 · see citing works
ChatGPT: Vision and challenges
Gill, S. S., & Kaur, R. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.05.004 · Cited by 265 · see citing works
“ChatGPT is mostly used for data analysis applications, such as gathering data from scientific research papers [6].”
BackgroundMethodInfluentialSohail, S. S., Farhat, F., Himeur, Y., Nadeem, M., Madsen, D. Ø., Singh, Y., Atalla, S., & Mansoor, W. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101675 · Cited by 192 · see citing works
“A few authors also questioned ChatGPT’s efficiency in cyber security (Mijwil et al., 2023) and its potential for biases due to algorithms (Wang, 2023b), region (Wang et al., 2023), and language (Seghier, 2023).”
From Fiction to Fact: The Growing Role of Generative AI in Business and Finance
Chen, B., Wu, Z., & Zhao, R. (2023) · Journal of Chinese Economic and Business Studies · doi:10.1080/14765284.2023.2245279 · Cited by 190 · see citing works
“An example of the current bias in generative AI is that ChatGPT is better at understanding content written in English than text in other languages (Seghier, 2023).”
BackgroundRevolutionizing Health Care: The Transformative Impact of Large Language Models in Medicine
Zhang, K., Meng, X., Yan, X., Ji, J., Liu, J., Xu, H., Zhang, H., Liu, D., Wang, J., Wang, X., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/59069 · Cited by 136 · see citing works
“This broad adoption must span various geographical regions and encompass diverse languages and cultural contexts, catering to users speaking English, Chinese, or local dialects [111,112].”
Nakaura, T., Ito, R., Ueda, D., Nozaki, T., Fushimi, Y., Matsui, Y., Yanagawa, M., Yamada, A., Tsuboyama, T., Fujima, N., et al. (2024) · Japanese Journal of Radiology · doi:10.1007/s11604-024-01552-0 · Cited by 127 · see citing works
“Although the precise performance of LLMs may exhibit variance based on language [46], these facts suggest that LLMs may be able to play a supplementary role even in quite specialized radiology work, even if only for text data at this point.”
BackgroundCadamuro, J., Cabitza, F., Debeljak, Ž., Bruyne, S. D., Frans, G., Pérez, S. M., Özdemir, H., Tolios, A., Carobene, A., & Padoan, A. (2023) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2023-0355 · Cited by 120 · see citing works
“As a major pitfall in using this chatbot, it should be considered that the richness of ChatGPT ’ s response and the intelligibility of its writing in some languages, including French and Arabic, are notably inferior to those in English [32].”
ResultZong, H., Li, J., Wu, E., Wu, R., Lu, J., & Shen, B. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05125-7 · Cited by 114 · see citing works
“Possible explanations for this performance difference include language and cultural differences, variations in examination content [26].”
BackgroundHuang, X., Estau, D., Liu, X., Yu, Y., Qin, J., & Li, Z. (2023) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.15896 · Cited by 117 · see citing works
“32 In particular, it is not open to the public in China, potentially leading to informational gaps regarding Chinese patent medicines.”
BackgroundChatGPT failed Taiwan’s Family Medicine Board Exam
Weng, T.-L., Wang, Y.-M., Chang, S., Chen, T.-J., & Hwang, S.-J. (2023) · Journal of the Chinese Medical Association · doi:10.1097/jcma.0000000000000946 · Cited by 100 · see citing works
Rossettini, G., Rodeghiero, L., Corradi, F., Cook, C., Pillastrini, P., Turolla, A., Castellini, G., Chiappinotto, S., Gianola, S., & Palese, A. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05630-9 · Cited by 103 · see citing works
Jin, H. K., Lee, H. E., & Kim, E. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05944-8 · Cited by 85 · see citing works
Madrid-García, A., Rosales, Z., Freites, D., Pérez-Sancristóbal, I., Pato-Cour, E., Plasencia‐Rodríguez, C., Osorio, L. C., Abásolo, L., León, L., Fernández‐Gutiérrez, B., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-49483-6 · Cited by 77 · see citing works
Improving radiology workflow using ChatGPT and artificial intelligence
Meşe, İ., Taşlıçay, C. A., & Sivrioğlu, A. K. (2023) · Clinical Imaging · doi:10.1016/j.clinimag.2023.109993 · Cited by 58 · see citing works
Watari, T., Takagi, S., Sakaguchi, K., Nishizaki, Y., Shimizu, T., Yamamoto, Y., & Tokuda, Y. (2023) · JMIR Medical Education · doi:10.2196/52202 · Cited by 54 · see citing works
“These limitations may lead to irrelevant or incorrect responses and conclusions [19,26].”
BackgroundWatters, C., & Lemański, M. K. (2023) · Frontiers in Big Data · doi:10.3389/fdata.2023.1224976 · Cited by 57 · see citing works
ChatGPT and Patient Information in Nuclear Medicine: GPT-3.5 Versus GPT-4
Currie, G. M., Robbie, S., & Tually, P. (2023) · Journal of Nuclear Medicine Technology · doi:10.2967/jnmt.123.266151 · Cited by 54 · see citing works
Applications of artificial intelligence and machine learning in clinical medicine: What lies ahead ?
Raj, G. M., Dananjayan, S., & Gudivada, K. K. (2024) · Medicine Advances · doi:10.1002/med4.62 · Cited by 41 · see citing works
Bisi, T., Risser, A., Clavert, P., Migаud, H., & Dаrtus, J. (2023) · Orthopaedics & Traumatology Surgery & Research · doi:10.1016/j.otsr.2023.103694 · Cited by 38 · see citing works
Tong, W., Guan, Y., Chen, J., Huang, X., Zhong, Y., Zhang, C., & Zhang, H. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1237432 · Cited by 38 · see citing works
Liu, Y., Park, Y., & Wang, H. (2024) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2024.104176 · Cited by 46 · see citing works
Artificial scholarship: LLMs in health professions education research
Ellaway, R. H., & Tolsgaard, M. (2023) · Advances in Health Sciences Education · doi:10.1007/s10459-023-10257-4 · Cited by 36 · see citing works
“( Seghier 2023 )”
One hundred important questions facing plant science derived using a large language model
Agathokleous, E., Rillig, M. C., Peñuelas, J., et al. (2023) · Trends in Plant Science · doi:10.1016/j.tplants.2023.06.008 · Cited by 28 · see citing works
Yao, Z., Duan, L., Xu, S., et al. (2025) · JMIR Medical Informatics · doi:10.2196/69485 · Cited by 27 · see citing works
“This disparity can be attributed to the preponderance of English-centric data in the training corpora, coupled with a limited inclusion of non-English content, leading to remarkable performance within English linguistic settings but less satisfactory results in non-English environments [10].”
Filetti, S., Fenza, G., & Gallo, A. (2024) · Endocrine · doi:10.1007/s12020-024-03977-z · Cited by 32 · see citing works
“Most LLMs are trained on extensive English datasets, leading to challenges for other languages: – Competitive disadvantage : English-centric LLMs may struggle with the nuances of other languages, leading to inaccurate translations and misinterpretations [50].”
ChatGPT in Medical Education: Bibliometric and Visual Analysis
Zhang, Y., Xie, X., & Xu, Q. (2025) · JMIR Medical Education · doi:10.2196/72356 · Cited by 29 · see citing works
Ming, S., Guo, Q., Cheng, W., & Lei, B. (2024) · JMIR Medical Education · doi:10.2196/52784 · Cited by 25 · see citing works
“While ChatGPT’s accuracy varies across languages [3], it has been tested on a series of medical exams like the Japanese National Medical Licensing Examination in languages including English [4], Chinese [5], Dutch [6], Japanese [7], and Korean [8].”
BackgroundLiu, C., & Chou, J. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105891 · Cited by 23 · see citing works
Adilmetova, G., Nassyrov, R., Meyerbekova, A., Karabay, A., Varol, H. A., & Chan, M. (2024) · Journal of Nutrition · doi:10.1016/j.tjnut.2024.12.018 · Cited by 24 · see citing works
Ozkan, E., Tekin, A., Ozkan, M. C., Cabrera, D., Niven, A. S., & Dong, Y. (2025) · JMIR Medical Education · doi:10.2196/58801 · Cited by 23 · see citing works
Nakajima, N., Fujimori, T., Furuya, M., Kanie, Y., Imai, H., Kita, K., Uemura, K., & Okada, S. (2024) · Cureus · doi:10.7759/cureus.56402 · Cited by 19 · see citing works
“5 was its lower accuracy in non-English languages [22], which was particularly evident in the Japanese medical licensing exam where GPT-3.”
BackgroundExploring the Current Applications and Effectiveness of ChatGPT in Nursing: An Integrative Review
Luo, Y., Miao, Y., Zhao, Y., Li, J., & Wu, Y. X. (2024) · Journal of Advanced Nursing · doi:10.1111/jan.16628 · Cited by 22 · see citing works
ChatGPT as an effective tool for quality evaluation of radiomics research
Meşe, İ., & Koçak, B. (2024) · European Radiology · doi:10.1007/s00330-024-11122-7 · Cited by 19 · see citing works
Lee, G. U., Hong, D. Y., Kim, S. Y., Kim, J. W., Lee, Y. H., Park, S. O., & Lee, K. R. (2024) · Medicine · doi:10.1097/md.0000000000037325 · Cited by 17 · see citing works
“[15] For example, ChatGPT-3.”
The impact of ChatGPT on human data collection: A case study involving typicality norming data
Heyman, T., & Heyman, G. (2023) · Behavior Research Methods · doi:10.3758/s13428-023-02235-w · Cited by 16 · see citing works
“On the flip side, the quality of the English output tends to be superior compared to other languages (Seghier, 2023), which could improve the correlation.”
BackgroundChatGPT: oportunidades y riesgos en la asistencia, docencia e investigación médica
Gutiérrez‐Cirlos, C., Carrillo‐Pérez, D. L., Bermudez‐Gonzalez, J. L., Hidrogo-Montemayor, I., Carrillo-Esper, R., & Mendiola, M. S. (2023) · Gaceta Médica de México · doi:10.24875/gmm.230001671 · Cited by 38 · see citing works
A retrospective evaluation of the potential of ChatGPT in the accurate diagnosis of acute stroke
Kuzan, B. N., Meşe, İ., Yaşar, S., et al. (2024) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2024.242892 · Cited by 16 · see citing works
Accuracy of large language models in answering ophthalmology board-style questions: A meta-analysis
Wu, J.-H., Nishida, T., & Liu, T. Y. A. (2024) · Asia-Pacific Journal of Ophthalmology · doi:10.1016/j.apjo.2024.100106 · Cited by 16 · see citing works
ChatGPT: opportunities and risks in the fields of medical care, teaching, and research
Gutiérrez‐Cirlos, C., Carrillo‐Pérez, D. L., Bermudez‐Gonzalez, J. L., Hidrogo-Montemayor, I., Carrillo-Esper, R., & Mendiola, M. S. (2023) · Gaceta Médica de México · doi:10.24875/gmm.m23000811 · Cited by 16 · see citing works
Using ChatGPT and other AI ‐assisted tools to improve manuscripts readability and language
Seghier, M. L. (2023) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22902 · Cited by 15 · see citing works
Self-citationRos‐Arlanzón, P., & Sempere, Á. P. (2024) · JMIR Medical Education · doi:10.2196/56762 · Cited by 14 · see citing works
“Unlike most existing literature that evaluates AI in English [28], our study probes ChatGPT’s performance in Spanish, a vital consideration for global medical applications given the variation in medical terminology and nuances across languages.”
Lukešová, A., & Jennings, P. (2026) · Innovation in Language Learning and Teaching · doi:10.1080/17501229.2025.2612532 · Cited by 16 · see citing works
Cognitive decline assessment in speakers of understudied languages
Hatahet, O., Röser, F., & Seghier, M. L. (2023) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1002/trc2.12432 · Cited by 13 · see citing works
“There is, for instance, an interest in the development of AI-based cognitive tests, 95 including chatbots powered by the like of ChatGPT to conduct cognitive tests, butthesechatbotsbasedonlargelanguagemodelsdonotperformwell in all languages (e.g., Seghier 96 ).”
MethodSelf-citationPerformance of GPT-3.5 and GPT-4 on the Korean Pharmacist Licensing Examination: Comparison Study
Jin, H. K., & Kim, E. Y. (2024) · JMIR Medical Education · doi:10.2196/57451 · Cited by 11 · see citing works
“Moreover, disparity in performance might be attributed to differences in language and culture, as well as variations in the content of the examinations [32].”
Beyond borders or building new walls?
Tran, H., & Stell, A. (2024) · Australian Review of Applied Linguistics · doi:10.1075/aral.24135.tra · Cited by 13 · see citing works
Using A Google Web Search Analysis to Assess the Utility of ChatGPT in Stem Cell Therapy
Chen, L., Li, H., Su, Y., Yang, Z., He, Z., Wang, D., Li, J. J., & Xing, D. (2023) · Stem Cells Translational Medicine · doi:10.1093/stcltm/szad074 · Cited by 10 · see citing works
“26 The accuracy of information provided by ChatGPT is crucial for clinicians and patients in decision-making and for patients in prediagnosis education, including in the area of stem cell therapy.”
BackgroundBarrot, J. S. (2024) · Springer eBooks · doi:10.1007/978-3-031-67667-3_13 · Cited by 10 · see citing works
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The ChatGPT revolution: Reshaping education through artificial intelligence
Kaur, K., Shilpa, & Jindal, R. (2024) · AIP Conference Proceedings · doi:10.1063/5.0221430 · Cited by 7 · see citing works
New possibilities of artificial intelligence in medicine: a narrative review
Litvin, A., Стома, И. О., Михайловна, Ш. Т., Rumovskaya, S. B., & Kyovalev, A. A. (2024) · Health and Ecology Issues · doi:10.51523/2708-6011.2024-21-1-01 · Cited by 8 · see citing works
Luo, Y., Miao, Y., Zhao, Y., Li, J., Chen, Y., Yue, Y., & Wu, Y. X. (2024) · JMIR Formative Research · doi:10.2196/63188 · Cited by 8 · see citing works
“Previous studies have found that ChatGPT could provide relatively precise health information in the English context [13,14].”
Li, Z., Yan, C., Cao, Y., Gong, A., Li, F., & Zeng, R. (2025) · Scientific Reports · doi:10.1038/s41598-025-04309-5 · Cited by 7 · see citing works
“Recent studies have highlighted the importance of investigating cross-linguistic variations in LLM performance 7,38 .”
Kottlors, J., Iuga, A.-I., Blüthgen, C., Bressem, K. K., Kather, J. N., Moy, L. P., Wald, C., Wang, W., Liu, T., Ranschaert, E. R., et al. (2026) · Radiology · doi:10.1148/radiol.250913 · Cited by 12 · see citing works
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Lee, J. K., Choi, S., Park, S., Hwang, S., & Cho, D. (2025) · Annals of Laboratory Medicine · doi:10.3343/alm.2024.0588 · Cited by 7 · see citing works
“This finding contrasts with those of Seghier, who re-ported that performance varied depending on the language used [21].”
Lian, L., Luo, X., Chipusu, K., Ashraf, M. A., Wong, K. K. L., & Zhang, W. (2026) · Bioengineering · doi:10.3390/bioengineering13010113 · Cited by 17 · see citing works
“Additionally, while ChatGPT shows a decline in non-English language capabilities compared to English [15], in this study, GPT-4o scored comparably to Ernie Bot 4.0 across all aspects, particularly excelling in Basic Medical knowledge—a universally applicable medical field—with an accuracy of…”
InfluentialDu, D., Yang, P., Zhao, L., An, W., Wang, Z., & Yu, S. (2023) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.3c01097 · Cited by 6 · see citing works
ChatGPT-A Generative Pre-Trained Transformer
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Dermatology “AI Babylon”: Cross-Language Evaluation of AI-Crafted Dermatology Descriptions
Karampinis, E., Zoumpourli, C.-M., Kontogianni, C., Arkoumanis, P. T., Koumaki, D., Mantzaris, D. H., Filippakis, K., Papadopoulou, M.-M., Theofili, M., Enechukwu, N. A., et al. (2026) · Medicina · doi:10.3390/medicina62010227 · Cited by 5 · see citing works
“[31] French and Arabic Testing of ChatGPT in a set of neuroscience questions.”
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Beyond the interface: Supporting beginner-level CEFR mediation and learner autonomy with ChatGPT
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The Game-Changing Impact of AI Chatbots on Education ChatGPT and Beyond
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Suitability of ChatGPT as a Source of Patient Information for Screening Mammography
Spuur, K. M., Currie, G. M., Al‐Mousa, D. S., & Pape, R. (2024) · Health Promotion Practice · doi:10.1177/15248399241285060 · Cited by 5 · see citing works
“ChatGPT might be better suited to language conversion of exemplar clinical patient information sheets where English is a second language, although ChatGPT has been reported to be inferior in non-English language text generation (Seghier, 2023).”
Perspectives on the use of ChatGPT in academic publications
Kardeş, G., & Oran, N. T. (2024) · Science and Public Policy · doi:10.1093/scipol/scae079 · Cited by 4 · see citing works
Conducting sociolinguistic interviews via generative AI: A methods tutorial
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“The lack of culturally specific knowledge and the latest medical literature and data in other languages is a significant limitation of ChatGPT, which may lead to irrelevant or incorrect answers and conclusions [33,34].”
LLMs for Question-Answer and Synthetic Data Generation and Evaluation
Balyan, R., Thompson, K., & Iacobelli, F. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98284-2_2 · Cited by 3 · see citing works
Evaluating the performance of ChatGPT in clinical pharmacyPreprint
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Incorporating ChatGPT Into Online Discussions in a Literacy Course
Hu, R., & Lin, X. Y. (2025) · International Journal of Artificial Intelligence · doi:10.4018/ijaitl.366589 · Cited by 2 · see citing works
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“Previous studies have shown that language effects are particularly evident in tasks requiring specific cultural background knowledge or relying on the latest literature and data in other languages [28], which may further account for the differences in model performance.”
Bisi, T., Risser, A., Clavert, P., Migаud, H., & Dаrtus, J. (2023) · Revue de Chirurgie Orthopédique et Traumatologique · doi:10.1016/j.rcot.2023.09.014 · Cited by 3 · see citing works
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“5 shows reduced proficiency in non-English languages compared to its performance in English [10].”
BackgroundTeachers’ opinions on the application of corpora in ICT and AI-aided foreign language education
Polok, K., Drózd, A., & Przybysz-Zaremba, M. (2025) · The Journal of Educational Research · doi:10.1080/00220671.2025.2510381 · Cited by 2 · see citing works
“Due to the fact that it is a machine, not a living being, it seems necessary to attentively and very precisely inform the chatbot what it is expected to produce, but even then some (luckily, not too many) errors are possible (Seghier, 2023).”
Zhou, Z., Ji, X., Zhang, J., Zhao, Z., Hei, X., & Choo, K. R. (2025) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-93354-7_15 · Cited by 2 · see citing works
Cross-lingual effects of AI-generated content on human work
Koo, W. W.-Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-16650-w · Cited by 8 · see citing works
Chimbunde, P., & Moreeng, B. B. (2025) · TechTrends · doi:10.1007/s11528-025-01062-4 · Cited by 3 · see citing works
“Seghier (2023) writes limiting or forbidding ChatGPT will not solve the problem, rather it is preferable to accept it and establish explicit guidelines for its application in academic settings which is supported by the disruptive technology theory which argues for the adoption of new technologies.”
Balzotti, A., Gallelli, R., Renna, P., Ascione, A., Amati, I., Casolaro, M., & Depalo, L. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-82915-4_6 · Cited by 1 · see citing works
The Future of Artificial Intelligence and Artificial Intelligence in Primary Care
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A systematic review of ChatGPT in education and scientific research: Insights from a SWOT analysis
Abdullahi, A., Ahmed, M. Y., & Hussein, H. A. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.102720 · Cited by 2 · see citing works
Asking ChatGPT for Pattern Recommendations: EuroPLoP 2024 Focus Group Report
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AI-based ChatGPT Impact on Medical Writing and Publication
Mofan, L., Zhang, M. Y., Sun, M. Y., Cui, P. L., & Wang, P. S. (2023) · Advanced ultrasound in diagnosis and therapy · doi:10.37015/audt.2023.230013
Adilmetova, G., Nassyrov, R., Meyerbekova, A., Karabay, A., Varol, H. A., & Chan, M. (2024) · doi:10.2196/preprints.56322
Ros‐Arlanzón, P., & Sempere, Á. P. (2024) · doi:10.2196/preprints.56762
Global Healthcare Professionals’ Perceptions of Large Language Model Use In Practice (Preprint)Preprint
Ozkan, E., Tekin, A., Ozkan, M. C., Cabrera, D., Niven, A. S., & Dong, Y. (2024) · doi:10.2196/preprints.58801
ChatGPT and Unified State Exam in Computer Science
Popov, V. S. (2024) · International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE) · doi:10.1109/reepe60449.2024.10479881
“The USE tasks in Computer Science were offered in Russian without translation into English, which could also affect the result of the study [23].”
MethodCurrent Use of Generative Artificial Intelligence in Pharmacy Practice: A Literature Mini-review
Kiyomiya, K., Aomori, T., Kawazoe, H., & Ohtani, H. (2025) · Iryo Yakugaku (Japanese Journal of Pharmaceutical Health Care and Sciences) · doi:10.5649/jjphcs.51.177 · Cited by 1 · see citing works
Asthana, A. K. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-3902-3.ch004
Performance of reasoning large language models on nephrology multiple-choice questionsPreprint
Kitano, F., Masaki, M., Ichikawa, D., Shibagaki, Y., & Noda, R. (2025) · medRxiv · doi:10.64898/2025.12.03.25341427
Rossettini, G., Rodeghiero, L., Corradi, F., Cook, C., Pillastrini, P., Turolla, A., Castellini, G., Chiappinotto, S., Gianola, S., & Palese, A. (2024) · Figshare · doi:10.6084/m9.figshare.26739498
Rossettini, G., Rodeghiero, L., Corradi, F., Cook, C., Pillastrini, P., Turolla, A., Castellini, G., Chiappinotto, S., Gianola, S., & Palese, A. (2024) · Figshare · doi:10.6084/m9.figshare.26739489
- Authors
- Seghier, M. L.
- Affiliations
- Khalifa University of Science and Technology (AE)
- Corresponding author
- Seghier, Mohamed L.
- ORCID
- Seghier, Mohamed L.
- Venue
- Nature
- Published
- 2023-03-07
- Volume
- 615
- Issue
- 7951
- Pages
- 216-216
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4323360926
- Cited by
- 108
- References
- 0
- Citations by year
- 2026: 9 · 2025: 23 · 2024: 41 · 2023: 34 · 2022: 1
- FWCI
- 10.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- ChatGPT, large language models, language bias, multilingual language models, language inequality
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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