This paper presents several strategies to automatically obtain additional examples for in-context learning of one-shot relation extraction. Specifically, we introduce a novel strategy for example selection, in which new examples are selected based on the similarity of their underlying syntactic-semantic structure to the provided one-shot example. We show that this method results in complementary word choices and sentence structures when compared to LLM-generated examples. When these strategies are combined, the resulting hybrid system achieves a more holistic picture of the relations of interest than either method alone. Our framework transfers well across datasets (FS-TACRED and FS-FewRel) and LLM families (Qwen and Gemma). Overall, our hybrid selection method consistently outperforms alternative strategies and achieves state-of-the-art performance on FS-TACRED and strong gains on a customized FewRel subset.
@article{arxiv.2601.20803,
title = {Structured Semantic Information Helps Retrieve Better Examples for In-Context Learning in Few-Shot Relation Extraction},
author = {Aunabil Chakma and Mihai Surdeanu and Eduardo Blanco},
journal= {arXiv preprint arXiv:2601.20803},
year = {2026}
}