  We study the performance of a commercially available large language model
(LLM) known as ChatGPT on math word problems (MWPs) from the dataset DRAW-1K.
To our knowledge, this is the first independent evaluation of ChatGPT. We found
that ChatGPT's performance changes dramatically based on the requirement to
show its work, failing 20% of the time when it provides work compared with 84%
when it does not. Further several factors about MWPs relating to the number of
unknowns and number of operations that lead to a higher probability of failure
when compared with the prior, specifically noting (across all experiments) that
the probability of failure increases linearly with the number of addition and
subtraction operations. We also have released the dataset of ChatGPT's
responses to the MWPs to support further work on the characterization of LLM
performance and present baseline machine learning models to predict if ChatGPT
can correctly answer an MWP. We have released a dataset comprised of ChatGPT's
responses to support further research in this area.
