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

Sequence-to-sequence state-of-the-art systems for dialogue state tracking
(DST) use the full dialogue history as input, represent the current state as a
list with all the slots, and generate the entire state from scratch at each
dialogue turn. This approach is inefficient, especially when the number of
slots is large and the conversation is long. In this paper, we propose Diable,
a new task formalisation that simplifies the design and implementation of
efficient DST systems and allows one to easily plug and play large language
models. We represent the dialogue state as a table and formalise DST as a table
manipulation task. At each turn, the system updates the previous state by
generating table operations based on the dialogue context. Extensive
experimentation on the MultiWoz datasets demonstrates that Diable (i)
outperforms strong efficient DST baselines, (ii) is 2.4x more time efficient
than current state-of-the-art methods while retaining competitive Joint Goal
Accuracy, and (iii) is robust to noisy data annotations due to the table
operations approach.

本文提出了 Diable 系统，通过将对话状态表示为数据表，并将 DST 形式化为表操作任务，简化了设计和实施高效的 DST 系统，通过在 MultiWoz 数据集上的实验证明，Diable 系统优于强大的高效 DST 基线，比当前最先进的方法更节省时间，而具有良好的鲁棒性，可以处理由于数据注释噪声导致表操作问题。