  Large Language Models (LLMs) are increasingly ubiquitous, yet their ability
to retain and reason about temporal information remains limited, hindering
their application in real-world scenarios where understanding the sequential
nature of events is crucial. Our study experiments with 12 state-of-the-art
models (ranging from 2B to 70B+ parameters) on a novel numerical-temporal
dataset, \textbf{TempUN}, spanning from 10,000 BCE to 2100 CE, to uncover
significant temporal retention and comprehension limitations. We propose six
metrics to assess three learning paradigms to enhance temporal knowledge
acquisition. Our findings reveal that open-source models exhibit knowledge gaps
more frequently, suggesting a trade-off between limited knowledge and incorrect
responses. Additionally, various fine-tuning approaches significantly improved
performance, reducing incorrect outputs and impacting the identification of
'information not available' in the generations. The associated dataset and code
are available at (https://github.com/lingoiitgn/TempUN).
