# Dynamic Few-Shot Learning for Knowledge Graph Question Answering Source: [Dynamic Few-Shot Learning for Knowledge Graph Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:59:03.375Z Authors: Jacopo D'Abramo, Andrea Zugarini, Paolo Torroni Published: 2024-07-01T15:59:17.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()