Language models, such as GPT-3.5 and ChatGPT, demonstrate remarkable
abilities to follow diverse human instructions and perform a wide range of
tasks. However, when probing language models using a range of basic
table-understanding tasks, we observe that today's language models are still
sub-optimal in many table-related tasks, likely because they are pre-trained
predominantly on \emph{one-dimensional} natural-language texts, whereas
relational tables are \emph{two-dimensional} objects.
In this work, we propose a new "\emph{table-tuning}" paradigm, where we
continue to train/fine-tune language models like GPT-3.5 and ChatGPT, using
diverse table-tasks synthesized from real tables as training data, with the
goal of enhancing language models' ability to understand tables and perform
table tasks. We show that our resulting Table-GPT models demonstrate (1) better
\emph{table-understanding} capabilities, by consistently outperforming the
vanilla GPT-3.5 and ChatGPT, on a wide-range of table tasks, including holdout
unseen tasks, and (2) strong \emph{generalizability}, in its ability to respond
to diverse human instructions to perform new table-tasks, in a manner similar
to GPT-3.5 and ChatGPT.

语言模型（如 GPT-3.5 和 ChatGPT）在执行各种任务和遵循多样化人类指令方面表现出卓越能力；然而，通过使用一系列基础的表格理解任务进行分析，我们发现当今的语言模型在许多与表格相关的任务上仍不完善，这可能是因为它们主要是在 “一维” 的自然语言文本上预训练的，而监管表格是 “二维” 的对象。因此，我们提出了一种新的 “表格调优” 范式，在这个范式中，我们通过使用合成自真实表格的多样化表格任务作为训练数据，继续训练 / 微调 GPT-3.5 和 ChatGPT 这类语言模型，以增强其理解表格和执行表格任务的能力。我们的实验结果表明，我们的 Table-GPT 模型在广泛的表格任务上始终优于普通 GPT-3.5 和 ChatGPT，并且在包括未见过的任务在内的情况下，它具有强大的泛化能力，可以以与 GPT-3.5 和 ChatGPT 类似的方式响应各种人类指令执行新的表格任务。