Recent breakthroughs in large language modeling have facilitated rigorous exploration of
their application in diverse tasks related to tabular data modeling, such as prediction, tabular …

Tabular data often refers to data that is organized in a table with rows and columns. We
observe that this data format is widely used on the Web and within enterprise data …

Large language models (LLMs) are becoming attractive as few-shot reasoners to solve
Natural Language (NL)-related tasks. However, there is still much to learn about how well …

Table-based reasoning with large language models (LLMs) is a promising direction to tackle
many table understanding tasks, such as table-based question answering and fact …

Semi-structured tables are ubiquitous. There has been a variety of tasks that aim to
automatically interpret, augment, and query tables. Current methods often require …

Recent progress in language model pre-training has achieved a great success via
leveraging large-scale unstructured textual data. However, it is still a challenge to apply pre …

Abstract Language models pretrained on large collections of tabular data have
demonstrated their effectiveness in several downstream tasks. However, many of these …

Inferring meta information about tables, such as column headers or relationships between
columns, is an active research topic in data management as we find many tables are …

Numerical reasoning over hybrid data containing both textual and tabular content (eg,
financial reports) has recently attracted much attention in the NLP community. However …

In the last few years, the natural language processing community has witnessed advances
in neural representations of free texts with transformer-based language models (LMs). Given …