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

• URL: http://arxiv.org/abs/2601.20803v1
• Date: Wed, 28 Jan 2026 17:48:58 GMT
• Title: Structured Semantic Information Helps Retrieve Better Examples for In-Context Learning in Few-Shot Relation Extraction
• Authors: Aunabil Chakma, Mihai Surdeanu, Eduardo Blanco,
• Abstract summary: 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.<n>When these strategies are combined, the resulting hybrid system achieves a more holistic picture of the relations of interest than either method alone.
• Score: 24.515561762205618
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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