A Few-Shot Approach for Relation Extraction Domain Adaptation using Large Language Models

• URL: http://arxiv.org/abs/2408.02377v1
• Date: Mon, 5 Aug 2024 11:06:36 GMT
• Title: A Few-Shot Approach for Relation Extraction Domain Adaptation using Large Language Models
• Authors: Vanni Zavarella, Juan Carlos Gamero-Salinas, Sergio Consoli,
• Abstract summary: This paper experiments with leveraging in-context learning capabilities of Large Language Models to perform data annotation. We show that by using a few-shot learning strategy with structured prompts and only minimal expert annotation the presented approach can potentially support domain adaptation of a science KG generation model.
• Score: 1.3927943269211591
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge graphs (KGs) have been successfully applied to the analysis of complex scientific and technological domains, with automatic KG generation methods typically building upon relation extraction models capturing fine-grained relations between domain entities in text. While these relations are fully applicable across scientific areas, existing models are trained on few domain-specific datasets such as SciERC and do not perform well on new target domains. In this paper, we experiment with leveraging in-context learning capabilities of Large Language Models to perform schema-constrained data annotation, collecting in-domain training instances for a Transformer-based relation extraction model deployed on titles and abstracts of research papers in the Architecture, Construction, Engineering and Operations (AECO) domain. By assessing the performance gain with respect to a baseline Deep Learning architecture trained on off-domain data, we show that by using a few-shot learning strategy with structured prompts and only minimal expert annotation the presented approach can potentially support domain adaptation of a science KG generation model.

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