Alongside huge volumes of research on deep learning models in NLP in the recent years,
there has been much work on benchmark datasets needed to track modeling progress …

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

Few-shot learning is a challenging task that requires language models to generalize from
limited examples. Large language models like GPT-3 and PaLM have made impressive …

Large Language Models (LLMs) are becoming increasingly smart and autonomous,
targeting real-world pragmatic missions beyond traditional NLP tasks. As a result, there has …

Mathematical reasoning, a core ability of human intelligence, presents unique challenges for
machines in abstract thinking and logical reasoning. Recent large pre-trained language …

Large language models (LLMs) have achieved remarkable performance on a variety of
natural language understanding tasks. However, existing benchmarks are inadequate in …

The sheer volume of financial statements makes it difficult for humans to access and analyze
a business's financials. Robust numerical reasoning likewise faces unique challenges in this …

Hybrid data combining both tabular and textual content (eg, financial reports) are quite
pervasive in the real world. However, Question Answering (QA) over such hybrid data is …

Recent literature has shown that large language models (LLMs) are generally excellent few-
shot reasoners to solve text reasoning tasks. However, the capability of LLMs on table …

Multihop reasoning remains an elusive goal as existing multihop benchmarks are known to
be largely solvable via shortcuts. Can we create a question answering (QA) dataset that, by …