The ability to understand and work with numbers (numeracy) is critical for many complex
reasoning tasks. Currently, most NLP models treat numbers in text in the same way as other …

Word embedding models are typically able to capture the semantics of words via the
distributional hypothesis, but fail to capture the numerical properties of numbers that appear …

Large language models (LLMs) have revolutionized NLP by solving downstream tasks with
little to no labeled data. Despite their versatile abilities, the larger question of their ability to …

Reading comprehension models have been successfully applied to extractive text answers,
but it is unclear how best to generalize these models to abstractive numerical answers. We …

Given the ubiquitous nature of numbers in text, reasoning with numbers to perform simple
calculations is an important skill of AI systems. While many datasets and models have been …

Large pre-trained language models (LMs) are known to encode substantial amounts of
linguistic information. However, high-level reasoning skills, such as numerical reasoning …

Large Language Models (LLMs) have limited performance when solving arithmetic
reasoning tasks and often provide incorrect answers. Unlike natural language …

Tables are often created with hierarchies, but existing works on table reasoning mainly focus
on flat tables and neglect hierarchical tables. Hierarchical tables challenge existing methods …

Math word problem (MWP) solving faces a dilemma in number representation learning. In
order to avoid the number representation issue and reduce the search space of feasible …

Large Language Models (LLMs) have demonstrated exceptional capabilities in various
natural language tasks, often achieving performances that surpass those of humans …