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arXiv · 2601.20803

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

Aunabil Chakma·Mihai Surdeanu·Eduardo Blanco
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Abstract

This paper presents several strategies to automatically obtain additional examples for in-context learning, effectively transforming relation extraction from a 1-shot to a few-shot setting. 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 1-shot example. We show that our strategy results in complementary word choices and sentence structures compared to LLM-generated examples. When both 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 system consistently outperforms alternative strategies achieving state-of-the-art performance on FS-TACRED and strong gains on a customized FewRel subset.

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Aunabil Chakma, Mihai Surdeanu, Eduardo Blanco. 2026-01-28. Structured Semantic Information Helps Retrieve Better Examples for In-Context Learning Applied to Few-Shot Relation Extraction. https://arxiv.org/abs/2601.20803

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