Improving Relation Extraction with End-to-End Trainable Retrieval-Augmented Generation (ETRAG)
This research paper introduces ETRAG, a novel approach for relation extraction that enhances retrieval-augmented generation by enabling end-to-end training of the retriever, leading to improved performance, especially in low-resource settings.
Retrieval-Augmented Generation-based Relation Extraction: Enhancing Performance through Relevant Sentence Integration
Retrieval-Augmented Generation-based Relation Extraction (RAG4RE) approach can outperform traditional Relation Extraction methods by integrating relevant example sentences into the prompt, mitigating hallucination issues in Large Language Models.
Few-shot Relation Extraction with CoT-ER Approach
Large language models can effectively perform few-shot relation extraction tasks with the CoT-ER approach, outperforming fully-supervised methods.
Verbesserung der Darstellung von Beziehungen mit Multi-View Decoupling Learning in niedrig-ressourcenbasierten Prompt-basierten Ansätzen
MVRE verbessert die Darstellung von Beziehungen in niedrig-ressourcenbasierten Prompt-basierten Ansätzen durch Multi-View Decoupling Learning.
© 2024 by Linnk AI