University of Oxford.
Independent Researcher.
Am J Bioeth. 2023 Oct;23(10):28-41. doi: 10.1080/15265161.2023.2233356. Epub 2023 Jul 24.
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.
在本文中，我们探讨了通过在自己先前发表的作品上微调大型语言模型（此处为 GPT-3）来提高学术论文质量和产生新想法的潜力，即 AUTOGEN（“AI 独特定制输出生成器”）。我们开发、测试并描述了三个基于当前三位作者（SBM、BDE、JS）先前学术著作的不同 AUTOGEN 模型，以及一个基于三位作者所有作品的第四个模型。我们的 AUTOGEN 模型在质量上的变化比基础 GPT-3 模型更大，许多输出在格式、风格、整体质量和新想法产生方面都优于基础模型。作为原理验证，我们展示并讨论了现有和假设研究论文中 AUTOGEN 编写的部分。我们进一步讨论了与个性化学术论文和想法生成器相关的伦理机会、关注问题和开放性问题。个性化的像 AUTOGEN 这样的 LLM 的伦理机会包括提高生产力、保留写作风格和文化传统以及促进共识建立。然而，由于个性化 LLM 可能会降低输出多样性、侵犯隐私和知识产权以及促进抄袭或欺诈，因此也存在伦理问题。合著或多源训练模型的使用进一步使所有权和归属问题复杂化。开放性问题涉及到 LLM 输出的信用归咎不对称、授权协议在作者归因中的合法性以及数据贡献者共同归属的伦理含义。确保输出与源材料足够不同对于保持学术写作的伦理标准至关重要。这些机会、风险和开放性问题突显了在学术界使用个性化 LLM 所涉及的复杂伦理问题。我们还讨论了与将 AUTOGEN 风格的个性化 LLM 与其他 LLM（如 GPT-4）集成相关的一些开放性技术问题，以迭代改进生成文本。总之，我们认为 AUTOGEN 风格的个性化 LLM 在生成文本方面具有很大的潜力，在产生新想法方面的潜力则较小。如果适当解决相关的伦理问题，AUTOGEN 或与其他 LLM 结合使用，可以被视为一种强大的学术增强形式。
