AUTOGEN: A Personalized Large Language Model for Academic Enhancement—Ethics and Proof of Principle.
In this article, we explore the potential of enhancing academic prose and idea generation by fine-tuning a large language model (here, GPT-3) on one's own previously published writings: AUTOGEN ("AI Unique Tailored Output GENerator"). We develop, test, and describe three distinct AUTOGEN models trai...
| Publicado en: | American Journal of Bioethics Vol. 23; no. 10; pp. 28 - 42 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Oct2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=172995023&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 172995023 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15265161 FKZ jtl: American Journal of Bioethics issn: 15265161 maglogo: N pubinfo: dt: Oct2023 vid: 23 iid: 10 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 172995023 165467191 172995023 172995023 10.1080/15265161.2023.2233356 172995023 ppf: 28 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: AUTOGEN: A Personalized Large Language Model for Academic Enhancement—Ethics and Proof of Principle. aug: au: Porsdam Mann, Sebastian Earp, Brian D. Møller, Nikolaj Vynn, Suren Savulescu, Julian affil: University of Oxford sug: subj: Academic Performance Evaluation Natural Language Processing Artificial Intelligence Models, Educational Human Productivity Writing for Publication Methods Automation ab: In this article, we explore the potential of enhancing academic prose and idea generation by fine-tuning a large language model (here, GPT-3) on one's own previously published writings: AUTOGEN ("AI Unique Tailored Output GENerator"). We develop, test, and describe three distinct AUTOGEN models trained on the prior scholarly output of three of the current authors (SBM, BDE, JS), with a fourth model trained on the combined works of all three. Our AUTOGEN models demonstrate greater variance in quality than the base GPT-3 model, with many outputs outperforming the base model in format, style, overall quality, and novel idea generation. As proof of principle, we present and discuss examples of AUTOGEN-written sections of existing and hypothetical research papers. We further discuss ethical opportunities, concerns, and open questions associated with personalized academic prose and idea generators. Ethical opportunities for personalized LLMs such as AUTOGEN include increased productivity, preservation of writing styles and cultural traditions, and aiding consensus building. However, ethical concerns arise due to the potential for personalized LLMs to reduce output diversity, violate privacy and intellectual property rights, and facilitate plagiarism or fraud. The use of coauthored or multiple-source trained models further complicates issues surrounding ownership and attribution. Open questions concern a potential credit-blame asymmetry for LLM outputs, the legitimacy of licensing agreements in authorship ascription, and the ethical implications of coauthorship attribution for data contributors. Ensuring the output is sufficiently distinct from the source material is crucial to maintaining ethical standards in academic writing. These opportunities, risks, and open issues highlight the intricate ethical landscape surrounding the use of personalized LLMs in academia. We also discuss open technical questions concerning the integration of AUTOGEN-style personalized LLMs with other LLMs, such as GPT-4, for iterative refinement and improvement of generated text. In conclusion, we argue that AUTOGEN-style personalized LLMs offer significant potential benefits in terms of both prose generation and, to a lesser extent, idea generation. If associated ethical issues are appropriately addressed, AUTOGEN alone or in combination with other LLMs can be seen as a potent form of academic enhancement. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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