Recent literature has shown that large language models (LLMs) are generally excellent few-
shot reasoners to solve text reasoning tasks. However, the capability of LLMs on table …

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 …

Table-based reasoning has shown remarkable progress in a wide range of table-based
tasks. It is a challenging task, which requires reasoning over both free-form natural language …

This work presents a sparse-attention Transformer architecture for modeling documents that
contain large tables. Tables are ubiquitous on the web, and are rich in information. However …

Tables contain a significant portion of the world's structured information. The ability to
efficiently and accurately understand, process, reason about, analyze, and generate tabular …

Data visualization in the form of charts plays a pivotal role in data analysis, offering critical
insights and aiding in informed decision-making. Automatic chart understanding has …

Fact verification has attracted a lot of research attention recently, eg, in journalism,
marketing, and policymaking, as misinformation and disinformation online can sway one's …

Reasoning over tabular data requires both table structure understanding and a broad set of
table reasoning skills. Current models with table-specific architectures and pre-training …

Question answering requiring discrete reasoning, eg, arithmetic computing, comparison,
and counting, over knowledge is a challenging task. In this paper, we propose UniRPG, a …

Tables contrast with unstructured text data by its structure to organize the information. In this
paper, we investigate the efficiency of various LLMs in interpreting tabular data through …