人类与大模型协作比单打独斗更安全高效
Performance Gains of LLMs With Humans in a World of LLMs Versus Humans
- 提出人机协同新范式，取代传统的'人类对大模型'对比研究
- 强调在临床场景中构建跨模型迭代的安全使用策略
- 适合关注医疗AI落地、人机协作的从业者和研究者
当前大量研究致力于将大语言模型（LLMs）与人类专家进行比较，但'专家'定义模糊且随模型快速迭代而变化。若缺乏有效保障机制，大模型可能威胁长期建立的患者安全护理体系。推动大模型创新的核心动力来自社区研究，但若持续沿用'人类对大模型'的对立框架，将加速这一风险。因此，未来研究应聚焦于开发适用于快速演进的新型大模型、能持续保障安全性的临床应用场景。本文主张：不应再简单比较大模型与人类，而应发展促进人类与大模型高效协同的策略，实现近乎共生的工作模式。
原文摘要 · Abstract (English)
Currently, a considerable research effort is devoted to comparing LLMs to a group of human experts, where the term "expert" is often ill-defined or variable, at best, in a state of constantly updating LLM releases. Without proper safeguards in place, LLMs will threaten to cause harm to the established structure of safe delivery of patient care which has been carefully developed throughout history to keep the safety of the patient at the forefront. A key driver of LLM innovation is founded on community research efforts which, if continuing to operate under "humans versus LLMs" principles, will expedite this trend. Therefore, research efforts moving forward must focus on effectively characterizing the safe use of LLMs in clinical settings that persist across the rapid development of novel LLM models. In this communication, we demonstrate that rather than comparing LLMs to humans, there is a need to develop strategies enabling efficient work of humans with LLMs in an almost symbiotic manner.
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