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的大型语言模型正在快速发展机器学习研究领域，应用领域包括自然语言、生物学、化学和编程。超级规模和强化学习是使生成文本质量显着提高的关键，同时还使模型能够完成各种任务并进行推理。在本文中，我们提出一种智能代理系统，它结合了多个大型语言模型，用于自主设计、规划和执行科学实验。我们展示了代理的三个不同示例，其中最复杂的是成功地执行了催化交叉偶联反应。最后，我们讨论了此类系统的安全性问题，并提出了防止滥用的措施。