Structured Semantic Information Helps Retrieve Better Examples for In-Context Learning Applied to Few-Shot Relation Extraction
arXiv:2601.20803v2 Announce Type: replace 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 co
Ongoing research in improving Large Language Model (LLM) performance, particularly in data-scarce scenarios, drives innovation in example selection for in-context learning.
This development enhances the efficiency and accuracy of LLMs in few-shot learning, making them more practical for real-world applications with limited training data for specific tasks.
The ability to automatically select better examples based on structural semantic information significantly improves the performance of large language models in specialized low-data tasks like relation extraction.
- · AI developers
- · Companies with specialized data sets
- · SaaS providers leveraging LLMs
- · Companies relying on extensive manual data labeling
Improved performance of LLMs in specific, low-resource tasks, requiring fewer manually annotated examples.
Faster development and deployment of AI applications in domains where data annotation is expensive or scarce.
Increased adoption of LLMs across diverse industries, further accelerating the AI agents narrative as their capabilities expand into more challenging areas.
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