Large language models (LLMs) trained on a substantial corpus of human knowledge and literature productively work with a large array of facts from that corpus. Surprisingly, they are also able to re-create the behaviors of personae that are captured within the corpus. By forming teams of simulated personae, supplying contexts that set the stage, and providing gentle prompts, one can move through scenarios that elicit expert behavior to perform meaningful cognitive work. The power of this strategy is demonstrated with two examples, one attacking factuality of LLM responses and the other reproducing a very recently published result in quantum optics.

翻译：基于人类知识与文献的大规模语料训练的大语言模型（LLMs）能够高效处理该语料中的大量事实。令人惊讶的是，它们还能再现语料中蕴含的角色行为。通过构建模拟角色团队、设定情境背景并提供温和提示，可引导场景激发专家行为，从而执行有意义的认知工作。本文通过两个案例展示该策略的威力：其一针对LLM回复的事实准确性，其二复现了量子光学领域一项最新发表的成果。