Large Language Models on Tabular Data -- A Survey

arXiv:2402.17944v1 Announce Type: new
Abstract: Recent breakthroughs in large language modeling have facilitated rigorous exploration of their application in diverse tasks related to tabular data modeling, such as prediction, tabular data synthesis, question answering, and table understanding. Each task presents unique challenges and opportunities. However, there is currently a lack of comprehensive review that summarizes and compares the key techniques, metrics, datasets, models, and optimization approaches in this research domain. This survey aims to address this gap by consolidating recent …

abstract application arxiv challenges cs.cl data data modeling diverse exploration language language models large language large language models modeling opportunities prediction question question answering survey synthesis table tabular tabular data tasks type understanding