The advent of large language models trained on code (code LLMs) has led to significant
progress in language-to-code generation. State-of-the-art approaches in this area combine …

Though end-to-end neural approaches have recently been dominating NLP tasks in both
performance and ease-of-use, they lack interpretability and robustness. We propose Binder …

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 …

To facilitate conversational question answering (CQA) over hybrid contexts in finance, we
present a new dataset, named PACIFIC. Compared with existing CQA datasets, PACIFIC …

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 …

With the growing significance of data analysis, several studies aim to provide precise
answers to users' natural language questions from tables, a task referred to as tabular …

There are a growing number of table pre-training methods proposed for reasoning over
tabular data (eg, question answering, fact checking, and faithful text generation). However …

Table Question-Answering involves both understanding the natural language query and
grounding it in the context of the input table to extract relevant information. In this context …

Table Question Answering (TQA) presents a substantial challenge at the intersection of
natural language processing and data analytics. This task involves answering natural …

Table-and-text hybrid question answering (HybridQA) is a widely used and challenging NLP
task commonly applied in the financial and scientific domain. The early research focuses on …