Recent advances in large language models (LLMs) enable effective in-context learning (ICL) with many-shot examples, but at the cost of high computational demand due to longer input tokens. To address this, we propose cheat-sheet ICL, which distills the information from many-shot ICL into a concise textual summary (cheat sheet) used as the context at inference time. Experiments on challenging reasoning tasks show that cheat-sheet ICL achieves comparable or better performance than many-shot ICL with far fewer tokens, and matches retrieval-based ICL without requiring test-time retrieval. These findings demonstrate that cheat-sheet ICL is a practical alternative for leveraging LLMs in downstream tasks.
@article{arxiv.2509.20820,
title = {Distilling Many-Shot In-Context Learning into a Cheat Sheet},
author = {Ukyo Honda and Soichiro Murakami and Peinan Zhang},
journal= {arXiv preprint arXiv:2509.20820},
year = {2025}
}