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.

通过对大型语言模型（LLMs）在表格任务方面的应用进行全面的概述，本文除了涵盖传统的表格问答（Table QA）和事实验证等领域，还强调了表格操作和高级表格数据分析等新方面，并介绍了 LLMs 中的指导调优、提示和基于代理的方法，同时也提出了私有部署、高效推理和开发广泛基准的相关挑战。

表格处理的大语言模型：调查报告

Large Language Model for Table Processing: A Survey

Tables stored in databases and tables which are present in web pages and
articles account for a large part of semi-structured data that is available on
the internet. It then becomes pertinent to develop a modeling approach with
large language models (LLMs) that can be used to solve diverse table tasks such
as semantic parsing, question answering as well as classification problems.
Traditionally, there existed separate models specialized for each task
individually. It raises the question of how far can we go to build a unified
model that works well on some table tasks without significant degradation on
others. To that end, we attempt at creating a shared modeling approach in the
pretraining stage with encoder-decoder style LLMs that can cater to diverse
tasks. We evaluate our approach that continually pretrains and finetunes
different model families of T5 with data from tables and surrounding context,
on these downstream tasks at different model scales. Through multiple ablation
studies, we observe that our pretraining with self-supervised objectives can
significantly boost the performance of the models on these tasks. As an example
of one improvement, we observe that the instruction finetuned public models
which come specialized on text question answering (QA) and have been trained on
table data still have room for improvement when it comes to table specific QA.
Our work is the first attempt at studying the advantages of a unified approach
to table specific pretraining when scaled from 770M to 11B sequence to sequence
models while also comparing the instruction finetuned variants of the models.

通过使用大型语言模型，我们尝试创建一个共享的建模方法，在预训练阶段使用编码器 - 解码器风格的大型语言模型，以适用于各种表格任务，并观察到自我监督目标的预训练可以显著提升模型在这些任务上的性能.