Black-box Large Language Models (LLMs) have shown great power in solving various tasks and are considered general problem solvers. However, LLMs still fail in many specific tasks although understand the task instruction. In this paper, we focus on the problem of boosting the ability of black-box LLMs to solve downstream tasks. We propose ExpNote, an automated framework to help LLMs better adapt to unfamiliar tasks through reflecting and noting experiences from training data and retrieving them from external memory during testing. We evaluate ExpNote on multiple tasks and the experimental results demonstrate that the proposed method significantly improves the performance of black-box LLMs. The data and code are available at https://github.com/forangel2014/ExpNote

翻译：黑盒大语言模型在解决各类任务中展现出强大能力，被视为通用问题求解器。然而，尽管理解任务指令，大语言模型在许多特定任务中仍会失败。本文聚焦于提升黑盒大语言模型解决下游任务的能力。我们提出ExpNote，一个自动化框架，通过从训练数据中反思并记录经验，并在测试时从外部记忆中检索这些经验，以帮助大语言模型更好地适应陌生任务。我们在多个任务上评估ExpNote，实验结果表明所提方法显著提升了黑盒大语言模型的性能。数据和代码可在 https://github.com/forangel2014/ExpNote 获取。