  Large language models present opportunities for innovative Question Answering
over Knowledge Graphs (KGQA). However, they are not inherently designed for
query generation. To bridge this gap, solutions have been proposed that rely on
fine-tuning or ad-hoc architectures, achieving good results but limited
out-of-domain distribution generalization. In this study, we introduce a novel
approach called Dynamic Few-Shot Learning (DFSL). DFSL integrates the
efficiency of in-context learning and semantic similarity and provides a
generally applicable solution for KGQA with state-of-the-art performance. We
run an extensive evaluation across multiple benchmark datasets and architecture
configurations.
