Structured Semantic Information Helps Retrieve Better Examples for In-Context Learning in Few-Shot Relation Extraction

TL;DR

This paper introduces a novel example selection strategy based on syntactic-semantic similarity for in-context learning in few-shot relation extraction, improving performance across datasets and models.

Contribution

The paper proposes a new hybrid example selection method leveraging syntactic-semantic similarity, enhancing few-shot relation extraction performance.

Findings

Hybrid selection outperforms alternative strategies.

Achieves state-of-the-art on FS-TACRED.

Shows strong gains on FewRel subset.

Abstract

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.