A Retrieval-Augmented Framework for Tabular Interpretation with Large Language Model

Mengyi Yan, Weilong Ren, Yaoshu Wang, Jianxin Li

Abstract: Relational tables on the web hold a vast amount of knowledge, and it is critical for machine learning models to capture the semantics of these tables such that the models can achieve good performance on table interpretation tasks, such as entity linking, column type annotation and relation extraction. However, it is very challenging for ML models to process a large amount of tables and/or retrieve inter-table context information from the tables. Instead, existing works usually rely on heavily engineered features, user-defined rules or pre-training corpus.