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，尽管能理解任务指令，大语言模型在许多特定任务中仍存在失败案例。本文聚焦于提升黑盒大语言模型在下游任务中能力的问题，提出ExpNote——一种自动化框架，通过从训练数据中反思并记录经验，并在测试阶段从外部存储中检索这些经验，帮助大语言模型更好地适应不熟悉的任务。我们在多个任务上评估了ExpNote，实验结果表明，该方法显著提升了黑盒大语言模型的性能。数据和代码可于https://github.com/forangel2014/ExpNote获取。