  The web serves as a global repository of knowledge, used by billions of
people to search for information. Ensuring that users receive the most relevant
and up-to-date information, especially in the presence of multiple versions of
web content from different time points remains a critical challenge for
information retrieval. This challenge has recently been compounded by the
increased use of question answering tools trained on Wikipedia or web content
and powered by large language models (LLMs) which have been found to make up
information (or hallucinate), and in addition have been shown to struggle with
the temporal dimensions of information. Even Retriever Augmented Language
Models (RALMs) which incorporate a document database to reduce LLM
hallucination are unable to handle temporal queries correctly. This leads to
instances where RALMs respond to queries such as "Who won the Wimbledon
Championship?", by retrieving document passages related to Wimbledon but
without the ability to differentiate between them based on how recent they are.
  In this paper, we propose and evaluate, TempRALM, a temporally-aware
Retriever Augmented Language Model (RALM) with few-shot learning extensions,
which takes into account both semantically and temporally relevant documents
relative to a given query, rather than relying on semantic similarity alone. We
show that our approach results in up to 74% improvement in performance over the
baseline RALM model, without requiring model pre-training, recalculating or
replacing the RALM document index, or adding other computationally intensive
elements.
