Large Language Model for Table Processing: A Survey
Weizheng Lu Jiaming Zhang Jing Zhang Yueguo Chen
Tables, typically two-dimensional and structured to store large amounts of
data, are essential in daily activities like database queries, spreadsheet
calculations, and generating reports from web tables. Automating these
table-centric tasks with Large Language Models (LLMs) offers significant public
benefits, garnering interest from academia and industry. This survey provides
an extensive overview of table tasks, encompassing not only the traditional
areas like table question answering (Table QA) and fact verification, but also
newly emphasized aspects such as table manipulation and advanced table data
analysis. Additionally, it goes beyond the early strategies of pre-training and
fine-tuning small language models, to include recent paradigms in LLM usage.
The focus here is particularly on instruction-tuning, prompting, and
agent-based approaches within the realm of LLMs. Finally, we highlight several
challenges, ranging from private deployment and efficient inference to the
development of extensive benchmarks for table manipulation and advanced data
analysis.