Tables, typically two-dimensional and structured to store large amounts of data, are
essential in daily activities like database queries, spreadsheet manipulations, Web table …

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 …

The capability gap between open-source and closed-source large language models (LLMs)
remains a challenge in text-to-SQL tasks. In this paper, we introduce a synthetic data …

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 …

While large language models (LLMs) like ChatGPT have shown impressive capabilities in
Natural Language Processing (NLP) tasks, a systematic investigation of their potential in this …

Recent advances in fine-tuning large language models (LLMs) have greatly enhanced their
usage in domain-specific tasks. Despite the success, fine-tuning continues to rely on …

Recent advancements in language models (LMs) have notably enhanced their ability to
reason with tabular data, primarily through program-aided mechanisms that manipulate and …

Advances in large language models (LLMs) have spurred research into enhancing their
reasoning capabilities, particularly in math-rich STEM documents. While LLMs can generate …

The real estate market relies heavily on structured data, such as property details, market
trends, and price fluctuations. However, the lack of specialized Tabular Question Answering …

Abstract Large Language Models (LLMs) have significantly advanced QA tasks through in-
context learning but often suffer from hallucinations. Attributing supporting evidence …