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      Artificial Hallucinations in ChatGPT: Implications in Scientific Writing

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      Author(s): 1 , 2 , 3 , , 3
      Editor(s): ,
      Publication date (Electronic, pub):
      Publication date (Electronic, collection):
      Journal: Cureus
      Publisher: Cureus
      Keywords: artificial intelligence and writing, artificial intelligence and education, chatgpt, chatbot, artificial intelligence in medicine

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          Abstract

          While still in its infancy, ChatGPT (Generative Pretrained Transformer), introduced in November 2022, is bound to hugely impact many industries, including healthcare, medical education, biomedical research, and scientific writing. Implications of ChatGPT, that new chatbot introduced by OpenAI on academic writing, is largely unknown. In response to the Journal of Medical Science (Cureus) Turing Test - call for case reports written with the assistance of ChatGPT, we present two cases one of homocystinuria-associated osteoporosis, and the other is on late-onset Pompe disease (LOPD), a rare metabolic disorder. We tested ChatGPT to write about the pathogenesis of these conditions. We documented the positive, negative, and rather troubling aspects of our newly introduced chatbot’s performance.

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          ChatGPT listed as author on research papers: many scientists disapprove

          Chris Stokel-Walker (2023)
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            Survey of Hallucination in Natural Language Generation

            Ziwei Ji, Nayeon Lee, Rita Frieske … (2022)
            Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation. However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which degrades the system performance and fails to meet user expectations in many real-world scenarios. To address this issue, many studies have been presented in measuring and mitigating hallucinated texts, but these have never been reviewed in a comprehensive manner before. In this survey, we thus provide a broad overview of the research progress and challenges in the hallucination problem in NLG. The survey is organized into two parts: (1) a general overview of metrics, mitigation methods, and future directions; and (2) an overview of task-specific research progress on hallucinations in the following downstream tasks, namely abstractive summarization, dialogue generation, generative question answering, data-to-text generation, and machine translation. This survey serves to facilitate collaborative efforts among researchers in tackling the challenge of hallucinated texts in NLG.
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              Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers

              Catherine A. Gao, Frederick M. Howard, Nikolay S. Markov … (2022)
              Background Large language models such as ChatGPT can produce increasingly realistic text, with unknown information on the accuracy and integrity of using these models in scientific writing. Methods We gathered ten research abstracts from five high impact factor medical journals (n=50) and asked ChatGPT to generate research abstracts based on their titles and journals. We evaluated the abstracts using an artificial intelligence (AI) output detector, plagiarism detector, and had blinded human reviewers try to distinguish whether abstracts were original or generated. Results All ChatGPT-generated abstracts were written clearly but only 8% correctly followed the specific journal’s formatting requirements. Most generated abstracts were detected using the AI output detector, with scores (higher meaning more likely to be generated) of median [interquartile range] of 99.98% [12.73, 99.98] compared with very low probability of AI-generated output in the original abstracts of 0.02% [0.02, 0.09]. The AUROC of the AI output detector was 0.94. Generated abstracts scored very high on originality using the plagiarism detector (100% [100, 100] originality). Generated abstracts had a similar patient cohort size as original abstracts, though the exact numbers were fabricated. When given a mixture of original and general abstracts, blinded human reviewers correctly identified 68% of generated abstracts as being generated by ChatGPT, but incorrectly identified 14% of original abstracts as being generated. Reviewers indicated that it was surprisingly difficult to differentiate between the two, but that the generated abstracts were vaguer and had a formulaic feel to the writing. Conclusion ChatGPT writes believable scientific abstracts, though with completely generated data. These are original without any plagiarism detected but are often identifiable using an AI output detector and skeptical human reviewers. Abstract evaluation for journals and medical conferences must adapt policy and practice to maintain rigorous scientific standards; we suggest inclusion of AI output detectors in the editorial process and clear disclosure if these technologies are used. The boundaries of ethical and acceptable use of large language models to help scientific writing remain to be determined.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Cureus
                Journal ID (iso-abbrev): Cureus
                Journal ID (issn): 2168-8184
                Title: Cureus
                Publisher: Cureus (Palo Alto (CA) )
                ISSN (Electronic): 2168-8184
                Publication date (Electronic, pub): 19 February 2023
                Publication date (Electronic, collection): February 2023
                Volume: 15
                Issue: 2
                Electronic Location Identifier: e35179
                Affiliations
                [1 ] Internal Medicine, Kings County Hospital Center, Brooklyn, USA
                [2 ] Internal Medicine, Veterans Affairs Medical Center, Brooklyn, USA
                [3 ] Internal Medicine, State University of New York Downstate Medical Center, Brooklyn, USA
                Author notes
                Article
                DOI: 10.7759/cureus.35179
                PMC ID: 9939079
                PubMed ID: 36811129
                SO-VID: a8e0b958-e9b9-49d2-a07e-78b1803a28b7
                Copyright © Copyright © 2023, Alkaissi et al.
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date accepted : 19 February 2023
                Categories
                Subject: Endocrinology/Diabetes/Metabolism
                Subject: Internal Medicine
                Subject: Healthcare Technology

                Keywords: artificial intelligence and writing,artificial intelligence and education,chatgpt,chatbot,artificial intelligence in medicine
                Data availability:
                Keywords: artificial intelligence and writing, artificial intelligence and education, chatgpt, chatbot, artificial intelligence in medicine

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