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 …

Pre-training is prevalent in nowadays deep learning to improve the learned model's
performance. However, in the literature on federated learning (FL), neural networks are …

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 …

The development of Artificial Intelligence Generated Content (AIGC) has been facilitated by
advancements in model algorithms, scalable foundation model architectures, and the …

Modern database management systems (DBMS) expose hundreds of configurable knobs to
control system behaviours. Determining the appropriate values for these knobs to improve …

In-context learning (ICL) has emerged as a new approach to various natural language
processing tasks, utilizing large language models (LLMs) to make predictions based on …

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 …

Table-based reasoning has shown remarkable progress in combining deep models with
discrete reasoning, which requires reasoning over both free-form natural language (NL) …

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 …

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