一种尺寸并不适合所有情况：预测上下文学习中的示例数量
One size doesn't fit all: Predicting the Number of Examples for In-Context Learning
AI总结：
本文提出AICL方法，通过多标签分类器动态预测每个实例的上下文学习示例数量，替代固定k值的“一刀切”策略，在多个文本分类基准上相比标准ICL最高提升17%的性能。
摘要：
上下文学习（ICL）是指在大型语言模型（LLM）的提示中添加来自带标签训练集的少量局部示例，以有效控制生成过程，旨在提升下游任务性能的过程。现有的ICL方法对每个数据实例使用相同数量的示例（预先配置的超参数）。我们的工作通过动态预测每个数据实例在少样本推理中应使用的示例数量，缓解了这种“一刀切”方法的局限性。具体而言，我们采用一个多标签分类器，其参数使用训练集进行拟合，其中该训练集中每个实例的标签指示使用特定k值（从0到最大值的最近邻示例数量）是否会导致正确的k-shot下游预测。我们在多个文本分类基准上的实验表明，AICL相比标准ICL性能提升最高达17%。
英文摘要：
In-context learning (ICL) refers to the process of adding a small number of localized examples from a training set of labelled data to an LLM's prompt with an objective to effectively control the generative process seeking to improve the downstream task performance. Existing ICL approaches use an identical number of examples (a pre-configured hyper-parameter) for each data instance. Our work alleviates the limitations of this 'one fits all' approach by dynamically predicting the number of examples for each data instance to be used in few-shot inference with LLMs. In particular, we employ a multi-label classifier, the parameters of which are fitted using a training set, where the label for each instance in this training set indicates if using a specific value of k (number of most similar examples from 0 up to a maximum value) leads to correct k-shot downstream predictions. Our experiments on a number of text classification benchmarks show that AICL substantially outperforms standard ICL by up to 17%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
