  Large language models (LLMs) have significantly transformed the landscape of
artificial intelligence by demonstrating their ability in generating human-like
text across diverse topics. However, despite their impressive capabilities,
LLMs lack recent information and often employ imprecise language, which can be
detrimental in domains where accuracy is crucial, such as climate change. In
this study, we make use of recent ideas to harness the potential of LLMs by
viewing them as agents that access multiple sources, including databases
containing recent and precise information about organizations, institutions,
and companies. We demonstrate the effectiveness of our method through a
prototype agent that retrieves emission data from ClimateWatch
(https://www.climatewatchdata.org/) and leverages general Google search. By
integrating these resources with LLMs, our approach overcomes the limitations
associated with imprecise language and delivers more reliable and accurate
information in the critical domain of climate change. This work paves the way
for future advancements in LLMs and their application in domains where
precision is of paramount importance.
