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The answering quality of an aligned large language model (LLM) can bedrastically improved if treated with proper crafting of prompts. In this paper,we propose ExpertPrompting to elicit the potential of LLMs to answer asdistinguished experts. We first utilize In-Context Learning to automaticallysynthesize detailed and customized descriptions of the expert identity for eachspecific instruction, and then ask LLMs to provide answer conditioned on suchagent background. Based on this augmented prompting strategy, we produce a newset of instruction-following data using GPT-3.5, and train a competitiveopen-source chat assistant called ExpertLLaMA. We employ GPT4-based evaluationto show that 1) the expert data is of significantly higher quality than vanillaanswers, and 2) ExpertLLaMA outperforms existing open-source opponents andachieves 96\% of the original ChatGPT's capability. All data and theExpertLLaMA model will be made publicly available at\url{https://github.com/OFA-Sys/ExpertLLaMA}.

ExpertPrompting: Instructing Large Language Models to be Distinguished Experts