  Although LLMs have the potential to transform many fields, they still
underperform humans in reasoning tasks. Existing methods induce the model to
produce step-by-step calculations, but this research explores the question:
Does making the LLM analyze the question improve its performance? We propose a
novel prompting strategy called Question Analysis Prompting (QAP), in which the
model is prompted to explain the question in $n$ words before solving. The
value of $n$ influences the length of response generated by the model. QAP is
evaluated on GPT 3.5 Turbo and GPT 4 Turbo on arithmetic datasets GSM8K, AQuA,
and SAT and commonsense dataset StrategyQA. QAP is compared with other
state-of-the-art prompts including Chain-of-Thought (CoT), Plan and Solve
Prompting (PS+) and Take A Deep Breath (TADB). QAP outperforms all
state-of-the-art prompts on AQuA and SAT datasets on both GPT3.5 and GPT4. QAP
consistently ranks among the top-2 prompts on 75\% of the tests. A key factor
of QAP performance can be attributed to response length, where detailed
responses are beneficial when answering harder questions, but can negatively
affect easy questions.
