{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T21:12:54Z","timestamp":1778015574450,"version":"3.51.4"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T00:00:00Z","timestamp":1773619200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T00:00:00Z","timestamp":1773619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["AI Ethics"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s43681-026-01075-6","type":"journal-article","created":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T16:15:08Z","timestamp":1773677708000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A comparative study of ChatGPT in scientific writing assistance: accuracy, style, and hallucination patterns across GPT-4o, GPT-5.0, and GPT-5.1"],"prefix":"10.1007","volume":"6","author":[{"given":"Usama Bin","family":"Anjum","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kana","family":"Hirasawa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Urooj","family":"Fatima","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,16]]},"reference":[{"key":"1075_CR1","unstructured":"Vaswani, A., Shazeer, N., Parmar, N. et al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30 (2017)"},{"key":"1075_CR2","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown, T., Mann, B., Ryder, N., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877\u20131901 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1075_CR3","first-page":"27730","volume":"35","author":"L Ouyang","year":"2022","unstructured":"Ouyang, L., Wu, J., Jiang, X., et al.: Training language models to follow instructions with human feedback. Adv. Neural. Inf. Process. Syst. 35, 27730\u201327744 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1075_CR4","unstructured":"Achiam, J., Adler, S., Agarwal, S. et al.: Gpt-4 technical report. arXiv preprint arXiv:230308774 (2023)"},{"key":"1075_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.lindif.2023.102274","volume":"103","author":"E Kasneci","year":"2023","unstructured":"Kasneci, E., Se\u00dfler, K., K\u00fcchemann, S., et al.: ChatGPT for good? On opportunities and challenges of large language models for education. Learn. Individ. Differ. 103, 102274 (2023)","journal-title":"Learn. Individ. Differ."},{"key":"1075_CR6","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2305016120","volume":"120","author":"F Gilardi","year":"2023","unstructured":"Gilardi, F., Alizadeh, M., Kubli, M.: ChatGPT outperforms crowd workers for text-annotation tasks. Proc. Natl. Acad. Sci. U.S.A. 120, e2305016120 (2023)","journal-title":"Proc. Natl. Acad. Sci. U.S.A."},{"key":"1075_CR7","doi-asserted-by":"publisher","DOI":"10.4324\/9780203122761","volume-title":"Handbook of Automated Essay Evaluation","author":"MD Shermis","year":"2013","unstructured":"Shermis, M.D., Burstein, J.: Handbook of Automated Essay Evaluation. Routledge, NY (2013)"},{"key":"1075_CR8","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.asw.2013.11.007","volume":"19","author":"M Stevenson","year":"2014","unstructured":"Stevenson, M., Phakiti, A.: The effects of computer-generated feedback on the quality of writing. Assess. Writing. 19, 51\u201365 (2014)","journal-title":"Assess. Writing."},{"key":"1075_CR9","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1080\/14703297.2023.2190148","volume":"61","author":"DR Cotton","year":"2024","unstructured":"Cotton, D.R., Cotton, P.A., Shipway, J.R.: Chatting and cheating: ensuring academic integrity in the era of ChatGPT. Innov. Educ. Teach. Int. 61, 228\u2013239 (2024)","journal-title":"Innov. Educ. Teach. Int."},{"key":"1075_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3571730","volume":"55","author":"Z Ji","year":"2023","unstructured":"Ji, Z., Lee, N., Frieske, R., et al.: Survey of hallucination in natural language generation. ACM Comput. Surv. 55, 1\u201338 (2023)","journal-title":"ACM Comput. Surv."},{"key":"1075_CR11","doi-asserted-by":"crossref","unstructured":"Maynez, J., Narayan, S., Bohnet, B., McDonald, R.: On faithfulness and factuality in abstractive summarization. arXiv preprint arXiv:200500661 (2020)","DOI":"10.18653\/v1\/2020.acl-main.173"},{"key":"1075_CR12","doi-asserted-by":"crossref","unstructured":"Lin, S., Hilton, J., Evans, O.: Truthfulqa: measuring how models mimic human falsehoods. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, vol. 1: long papers, pp. 3214\u20133252 (2022)","DOI":"10.18653\/v1\/2022.acl-long.229"},{"key":"1075_CR13","first-page":"70293","volume":"36","author":"N Dziri","year":"2023","unstructured":"Dziri, N., Lu, X., Sclar, M., et al.: Faith and fate: Limits of transformers on compositionality. Adv. Neural. Inf. Process. Syst. 36, 70293\u201370332 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1075_CR14","unstructured":"Mialon, G., Dess\u00ec, R., Lomeli, M. et al.: Augmented language models: a survey. arXiv preprint arXiv:230207842 (2023)"},{"key":"1075_CR15","unstructured":"Rawte, V., Sheth, A., Das, A.: A survey of hallucination in large foundation models. arXiv preprint arXiv:230905922 (2023)"},{"key":"1075_CR16","first-page":"1","volume":"43","author":"L Huang","year":"2025","unstructured":"Huang, L., Yu, W., Ma, W., et al.: A survey on hallucination in large language models: principles, taxonomy, challenges, and open questions. ACM Trans. Inf. Syst. 43, 1\u201355 (2025)","journal-title":"ACM Trans. Inf. Syst."},{"key":"1075_CR17","doi-asserted-by":"crossref","unstructured":"Manakul, P., Liusie, A., Gales, M.: Selfcheckgpt: zero-resource black-box hallucination detection for generative large language models. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 9004\u20139017 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.557"},{"key":"1075_CR18","unstructured":"Bai, Y., Jones, A., Ndousse, K. et al.: Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:220405862 (2022)"},{"key":"1075_CR19","doi-asserted-by":"publisher","first-page":"1362","DOI":"10.1109\/JSTSP.2025.3579203","volume":"19","author":"B Malin","year":"2025","unstructured":"Malin, B., Kalganova, T., Boulgouris, N.: A review of faithfulness metrics for hallucination assessment in large language models. IEEE J. Sel. Top. Signal Process. 19, 1362 (2025)","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"1075_CR20","doi-asserted-by":"crossref","unstructured":"Alansari, A., Luqman, H.: Large language models hallucination: a comprehensive survey. arXiv preprint arXiv:251006265 (2025)","DOI":"10.1016\/j.cosrev.2026.100970"},{"key":"1075_CR21","unstructured":"Cossio, M.: A comprehensive taxonomy of hallucinations in large language models. arXiv preprint arXiv:250801781 (2025)"},{"key":"1075_CR22","unstructured":"Callison-Burch, C., Osborne, M., Koehn, P.: Re-evaluating the role of BLEU in machine translation research. In: 11th Conference of the European Chapter of the Association for Computational Linguistics, pp. 249\u2013256 (2006)"},{"key":"1075_CR23","unstructured":"Holtzman, A., Buys, J., Du, L. et al.: The curious case of neural text degeneration. arXiv preprint arXiv:190409751 (2019)"},{"key":"1075_CR24","doi-asserted-by":"crossref","unstructured":"Mitchell, M., Wu, S., Zaldivar, A. et al.: Model cards for model reporting. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 220\u2013229 (2019)","DOI":"10.1145\/3287560.3287596"},{"key":"1075_CR25","doi-asserted-by":"crossref","unstructured":"Bender, E.M., Gebru, T., McMillan-Major, A., Shmitchell, S.: On the dangers of stochastic parrots: can language models be too big?. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 610\u2013623 (2021)","DOI":"10.1145\/3442188.3445922"},{"key":"1075_CR26","unstructured":"Weidinger, L., Mellor, J., Rauh, M. et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:211204359 (2021)"}],"container-title":["AI and Ethics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43681-026-01075-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s43681-026-01075-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43681-026-01075-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T20:38:46Z","timestamp":1778013526000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s43681-026-01075-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,16]]},"references-count":26,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["1075"],"URL":"https:\/\/doi.org\/10.1007\/s43681-026-01075-6","relation":{},"ISSN":["2730-5953","2730-5961"],"issn-type":[{"value":"2730-5953","type":"print"},{"value":"2730-5961","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,16]]},"assertion":[{"value":"17 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This manuscript is the authors\u2019 original work and has not been published elsewhere nor submitted simultaneously to any other journal. All authors have read and approved the final version of the manuscript.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"During the preparation of this work, the author(s) used ChatGPT (OpenAI, San Francisco,CA, USA;\n                      \n                      ) to improve wording, clarity, text formatting, and grammar.After using this tool, the author(s) carefully reviewed and edited the content as needed and takefull responsibility for the content of the published article.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Generative AI and AI-assisted technologies"}}],"article-number":"204"}}