重点错位的临床大型语言模型

IF 18.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zining Luo, Haowei Ma, Zhiwu Li, Yuquan Chen, Yixin Sun, Aimin Hu, Jiang Yu, Yang Qiao, Junxian Gu, Hongying Li, Xuxi Peng, Dunrui Wang, Ying Liu, Zhenglong Liu, Jiebin Xie, Zhen Jiang, Gang Tian
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引用次数: 0

摘要

2024 年 9 月 12 日,OpenAI 发布了两款新的大型语言模型(LLM)--o1-preview 和 o1-mini,标志着商业 LLM 的竞争格局发生了重要变化,尤其是在推理能力方面。自 GPT-3.5 推出以来,OpenAI 已在两年内推出了 31 个 LLM。研究人员正在迅速将这些不断发展的商业模型应用于临床医学,取得的成果有时甚至超过了人类在特定任务中的表现。尽管这种成功令人鼓舞,但用于这些任务的模型的开发可能与临床实践的特点和需求不符。LLM 可分为开源和闭源两种。开源模型,如 Meta 的 Llama,允许开发人员自由访问源代码、训练数据和文档。相比之下,闭源模型只能通过官方渠道或应用编程接口(API)访问。最初,开源模型在 LLM 领域占据主导地位,直到 20201 年 OpenAI 的 GPT-3 发布,吸引了相当大的商业兴趣,并将重点转向闭源方法2。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Clinical large language models with misplaced focus

On 12 September 2024, OpenAI released two new large language models (LLMs) — o1-preview and o1-mini — marking an important shift in the competitive landscape of commercial LLMs, particularly concerning their reasoning capabilities. Since the introduction of GPT-3.5, OpenAI has launched 31 LLMs in two years. Researchers are rapidly applying these evolving commercial models in clinical medicine, achieving results that sometimes exceed human performance in specific tasks. Although such success is encouraging, the development of the models used for these tasks may not align with the characteristics and needs of clinical practice.

LLMs can be categorized as either open-source or closed-source. Open-source models, such as Meta’s Llama, allow developers to access source code, training data and documentation freely. By contrast, closed-source models are accessed only through official channels or application programming interfaces (APIs). Initially, open-source models dominated the LLM landscape, until the release of OpenAI’s GPT-3 in 20201, which attracted considerable commercial interest and shifted focus towards closed-source approaches2.

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来源期刊
CiteScore
36.90
自引率
2.10%
发文量
127
期刊介绍: Nature Machine Intelligence is a distinguished publication that presents original research and reviews on various topics in machine learning, robotics, and AI. Our focus extends beyond these fields, exploring their profound impact on other scientific disciplines, as well as societal and industrial aspects. We recognize limitless possibilities wherein machine intelligence can augment human capabilities and knowledge in domains like scientific exploration, healthcare, medical diagnostics, and the creation of safe and sustainable cities, transportation, and agriculture. Simultaneously, we acknowledge the emergence of ethical, social, and legal concerns due to the rapid pace of advancements. To foster interdisciplinary discussions on these far-reaching implications, Nature Machine Intelligence serves as a platform for dialogue facilitated through Comments, News Features, News & Views articles, and Correspondence. Our goal is to encourage a comprehensive examination of these subjects. Similar to all Nature-branded journals, Nature Machine Intelligence operates under the guidance of a team of skilled editors. We adhere to a fair and rigorous peer-review process, ensuring high standards of copy-editing and production, swift publication, and editorial independence.
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