用精简摘要替代长例子，让大模型推理更快更省资源。
Distilling Many-Shot In-Context Learning into a Cheat Sheet
- 将多示例学习信息压缩成一句话摘要作为上下文
- 仅用少量词就达到原方法的推理效果
- 适合需要快速部署的下游任务应用
大型语言模型（LLMs）在多示例上下文学习（many-shot ICL）中表现优异，但需大量输入标记，计算开销高。为此，我们提出「速记式」上下文学习（cheat-sheet ICL），将多示例信息提炼为简洁文本摘要，在推理时作为上下文使用。在复杂推理任务上的实验表明，该方法以远少于原方案的标记数，实现了与多示例 ICL 相当或更优的性能，并且无需测试时检索即可媲美基于检索的 ICL。结果证明，速记式 ICL 是利用大模型处理下游任务的一种实用替代方案。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。