A Few-Shot Approach for Relation Extraction Domain Adaptation using Large Language Models
<|reference_start|>A Few-Shot Approach for Relation Extraction Domain Adaptation using Large Language Models: 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.<|reference_end|>
@article{zavarella2024a,
    title={A Few-Shot Approach for Relation Extraction Domain Adaptation using
  Large Language Models},
    author={Vanni Zavarella and Juan Carlos Gamero-Salinas and Sergio Consoli},
    journal={arXiv preprint arXiv:2408.02377},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02377},
    primaryClass={cs.CL cs.AI}
}