Transformer-based large language models are rapidly advancing in the field of machine learning research, with applications spanning natural language, biology, chemistry, and computer programming. Extreme scaling and reinforcement learning from human feedback have significantly improved the quality of generated text, enabling these models to perform various tasks and reason about their choices. In this paper, we present an Intelligent Agent system that combines multiple large language models for autonomous design, planning, and execution of scientific experiments. We showcase the Agent's scientific research capabilities with three distinct examples, with the most complex being the successful performance of catalyzed cross-coupling reactions. Finally, we discuss the safety implications of such systems and propose measures to prevent their misuse.

翻译：基于Transformer的大型语言模型正快速推进机器学习研究领域，其应用涵盖自然语言、生物学、化学和计算机编程。极端规模扩展与基于人类反馈的强化学习显著提升了生成文本的质量，使这些模型能够执行多样任务并对其选择进行推理。本文提出一种智能代理系统，该系统整合多个大型语言模型，用于科学实验的自主设计、规划与执行。我们通过三个典型示例展示了该代理的科学研究能力，其中最为复杂的是成功完成了催化交叉偶联反应。最后，我们讨论了此类系统的安全性影响，并提出了防止其滥用的措施。