This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As
LLMs find applications across diverse domains, the reliability and accuracy of their outputs …

Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …

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

Answering complex factual questions has drawn a lot of attention. Researchers leverage
various data sources to support complex QA, such as unstructured texts, structured …

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 …

Fact verification has attracted a lot of attention in the machine learning and natural language
processing communities, as it is one of the key methods for detecting misinformation …

When answering complex questions, people can seamlessly combine information from
visual, textual and tabular sources. While interest in models that reason over multiple pieces …

We study open-domain question answering with structured, unstructured and semi-
structured knowledge sources, including text, tables, lists and knowledge bases. Departing …

In the last few years, the natural language processing community has witnessed advances
in neural representations of free texts with transformer-based language models (LMs). Given …