Remember This Event That Year? Assessing Temporal Information and
  Reasoning in Large Language Models
Himanshu Beniwal Kowsik Nandagopan D Mayank Singh
Large Language Models (LLMs) are increasingly becoming ubiquitous, yet their
ability to reason about and retain temporal information remains limited. This
hinders their application in real-world scenarios where understanding the
sequential nature of events is crucial. This paper experiments with
state-of-the-art models on a novel, large-scale temporal dataset,
\textbf{TempUN}, to reveal significant limitations in temporal retention and
reasoning abilities. Interestingly, closed-source models indicate knowledge
gaps more frequently, potentially suggesting a trade-off between uncertainty
awareness and incorrect responses. Further, exploring various fine-tuning
approaches yielded no major performance improvements. The associated dataset
and code are available at the following URL
(https://github.com/lingoiitgn/TempUN).