  In-context learning (ICL) empowers large language models (LLMs) to tackle new
tasks by using a series of training instances as prompts. Since generating the
prompts needs to sample from a vast pool of instances and annotate them (e.g.,
add labels in classification task), existing methods have proposed to select a
subset of unlabeled examples for annotation, thus enhancing the quality of
prompts and concurrently mitigating annotation costs. However, these methods
often require a long time to select instances due to their complexity,
hindering their practical viability. To address this limitation, we propose a
graph-based selection method, FastGAS, designed to efficiently identify
high-quality instances while minimizing computational overhead. Initially, we
construct a data similarity graph based on instance similarities. Subsequently,
employing a graph partitioning algorithm, we partition the graph into pieces.
Within each piece (i.e., subgraph), we adopt a greedy approach to pick the most
representative nodes. By aggregating nodes from diverse pieces and annotating
the corresponding instances, we identify a set of diverse and representative
instances for ICL. Compared to prior approaches, our method not only exhibits
superior performance on different tasks but also significantly reduces
selection time. In addition, we demonstrate the efficacy of our approach in
LLMs of larger sizes.
