REBEL: Relation Extraction By End-to-end Language generation

By Pere-Lluís Huguet Cabot, Roberto Navigli. Conference on Empirical Methods in Natural Language Processing, 2021

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BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining (2022, methodology): “We mainly compare to REBEL [24], a recently proposed end-to-end triplet extraction approach based on sequence-to-sequence model, which employs BART pre-trained model [53] as the backbone model, and further enhances it by pre-training on additional large relational triplet dataset created from…”
A study of generative large language model for medical research and healthcare (2023, neutral): “We compared the two GatorTronGPT models with four existing transformer models including GPT-2 43 , REBEL, REBEL-pt 25 , and BioGPT 18 on three biomedical tasks for end-to-end relation extraction using three benchmark datasets including drug-drug interaction 44 (DDI), BioCreative V chemical-disease r”
Retrieval And Structuring Augmented Generation with Large Language Models (2025, unknown): “Similarly, frameworks like REBEL [83] and ABSA [271] leverage pre-trained language models (PLMs) to translate raw text into structured knowledge.”