One of the ways Large Language Models (LLMs) are used to perform machine learning tasks is to provide them with a few examples before asking them to produce a prediction. This is a meta-learning process known as few-shot learning. In this paper, we use available Search-Based methods to optimise the number and combination of examples that can improve an LLM's estimation performance, when it is used to estimate story points for new agile tasks. Our preliminary results show that our SBSE technique improves the estimation performance of the LLM by 59.34% on average (in terms of mean absolute error of the estimation) over three datasets against a zero-shot setting.

翻译：大型语言模型（LLMs）执行机器学习任务的方式之一，是在要求其生成预测前提供少量示例。这种元学习过程被称为少样本学习。本文利用现有的基于搜索方法，优化示例的数量与组合，以提升LLM在估算新敏捷任务故事点时的性能。初步结果表明，与零样本设置相比，我们的SBSE技术平均将LLM的估算性能（以估算的平均绝对误差衡量）提升59.34%。