Table reasoning aims to generate inference results based on the user requirement and the
provided table. Enhancing the table reasoning capability of the model can aid in obtaining …

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 …

Abstract Long-form Table Question Answering (LFTQA) requires systems to generate
paragraph long and complex answers to questions over tabular data. While Large language …

Recent text and image foundation models are incredibly impressive, and these models are
attracting an ever-increasing portion of research resources. In this position piece we aim to …

The use of Retrieval-Augmented Generation (RAG) has improved Large Language Models
(LLMs) in collaborating with external data, yet significant challenges exist in real-world …

The integration of conversational agents into our daily lives has become increasingly
common, yet many of these agents cannot engage in deep interactions with humans …

Fact verification on tabular evidence incentivizes the use of symbolic reasoning models
where a logical form is constructed (eg, a LISP-style program), providing greater verifiability …

Schema matching--the task of finding matches between attributes across disparate data
sources with different tables and hierarchies--is critical for creating interoperable machine …

Tabular Representation Learning (TRL) and Large Language Models (LLMs) have become
established for tackling Question Answering (QA) and Semantic Parsing (SP) tasks on …

The open-domain text-to-SQL task aims to retrieve question-relevant tables from massive
databases and generate SQL. However, the performance of current methods is constrained …