ExpNote: Black-box Large Language Models are Better Task Solvers with
  Experience Notebook
Wangtao Sun Xuanqing Yu Shizhu He Jun Zhao Kang Liu
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