In this work, we develop a specialized language model with strong discrete reasoning
capabilities to tackle question answering (QA) over hybrid tabular and textual data in …

Question answering over hybrid contexts is a complex task, which requires the combination
of information extracted from unstructured texts and structured tables in various ways …

With the widespread application of temporal knowledge graph reasoning (TKGR) models,
there is an increasing demand to reduce the memory consumption and enhance the …

Question answering requiring numerical reasoning, which generally involves symbolic
operations such as sorting, counting, and addition, is a challenging task. To address such a …

Discrete reasoning over table-text documents (eg, financial reports) gains increasing
attention in recent two years. Existing works mostly simplify this challenge by manually …

Answering questions about hybrid data combining tables and text is challenging. Recent
research has employed encoder-tree decoder frameworks to simulate the reasoning …

Large Language models (LLMs) usually rely on extensive training datasets. In the financial
domain, creating numerical reasoning datasets that include a mix of tables and long text …

While Large Language Models (LLMs) have shown impressive capabilities in numerous
Natural Language Processing (NLP) tasks, they still struggle with financial question …

Abstract Financial Question Answering (QA) has emerged as a critical area of research,
aiming to develop intelligent systems capable of interpreting and answering complex …