ReTaT: A Unified Benchmark for Relation Extraction across Text and Table
ReTaT is a dataset that can be used to train and evaluate systems for extracting relations whose expression spans the two modalities and its quality and suitability were assessed for the joint table-text relation extraction task using Large Language Models (LLMs).
Mohamed Ettaleb, Thibault Ehrhart, Nathalie Aussenac-Gilles et al.
· 1 citation