Bridging the Semantic Gap Between Text and Table: A Case Study on NL2SQL
Lin Long Xijun Gu Xinjie Sun Wentao Ye Haobo Wang Sai Wu Gang Chen Junbo Zhao
The rise of Large Language Models (LLMs) has revolutionized numerous domains, yet these models still exhibit weakness in understanding structured tabular data.
Although the growing context window promises to accommodate a larger volume of table contents, it does not inherently improve the model's ability to understand the underlying structure and semantics of tabular data.
To bridge the semantic gap between **T**ext and **T**able, we propose **T**n**T**, a table-language model that features multimodal table representations to empower LLMs to effectively and efficiently abstract structure-enriched semantics from tabular data. 
**T**n**T** also introduces a scalable and efficient training pipeline, featuring novel self-supervised tasks, to integrate abstract tabular knowledge into the language modality.
Extensive experimental results on NL2SQL demonstrate a much better table understanding of **T**n**T**, which achieves up to **14.4** higher execution accuracy compared with traditional text-based table representations.
Large Language Model, Table Representation Learning, NL2SQL, Multimodal Learning
To bridge the gap between tabular and textual information, we propose TNT, a table-language model that empowers LLMs with the ability to effectively and efficiently extract structure-enriched semantics from tabular data.