TableMaster: A Recipe to Advance Table Understanding with Language Models

Understand

Tables serve as a fundamental format for representing structured relational data.

• While current language models (LMs) excel at many text-based tasks, they still face challenges in table understanding due to the complex characteristics of tabular data, such as their structured nature.
• In this paper, we aim to enhance LMs for improved table understanding.
• We identify four key challenges: 1) difficulty in locating target data, 2) deficiency in table semantics, 3) numerical inaccuracies in textual reasoning, and 4) semantic inflexibility in symbolic reasoning.