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 …

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 …

Many facts come with an expiration date, from the name of the President to the basketball
team Lebron James plays for. However, most language models (LMs) are trained on …

We introduce RealTime QA, a dynamic question answering (QA) platform that announces
questions and evaluates systems on a regular basis (weekly in this version). RealTime QA …

Despite the success of large language models (LLMs) in various natural language
processing (NLP) tasks, the stored knowledge in these models may inevitably be …

The goal of temporal relation extraction is to infer the temporal relation between two events
in the document. Supervised models are dominant in this task. In this work, we investigate …

Reasoning about time is of fundamental importance. Many facts are time-dependent. For
example, athletes change teams from time to time, and different government officials are …

Temporal Knowledge Graphs (Temporal KGs) extend regular Knowledge Graphs by
providing temporal scopes (start and end times) on each edge in the KG. While Question …

Question answering over knowledge graphs (KG-QA) is a vital topic in IR. Questions with
temporal intent are a special class of practical importance, but have not received much …

Large language models (LLMs) have shown superior performance without task-specific fine-
tuning. Despite the success, the knowledge stored in the parameters of LLMs could still be …