  While large language models (LLMs) such as ChatGPT and PaLM have demonstrated
remarkable performance in various language understanding and generation tasks,
their capabilities in complex reasoning and intricate knowledge utilization
still fall short of human-level proficiency. Recent studies have established
the effectiveness of prompts in steering LLMs towards generating desired
outputs. Building on these insights, we introduce a novel framework that
harnesses the potential of large-scale pre-trained language models, to
iteratively enhance performance of the LLMs. Our framework incorporates three
components: \textit{Normal CoT}, a \textit{Convincer}, and an
\textit{Answerer}. It processes the output of a typical few-shot
chain-of-thought prompt, assesses the correctness of the response, scrutinizes
the answer, refines the reasoning, and ultimately produces a new solution.
Experimental results on the 7 datasets of miscellaneous problems validate the
efficacy of the Self-Convince framework, achieving substantial improvements
compared to the baselines. This study contributes to the burgeoning body of
research focused on integrating pre-trained language models with tailored
prompts and iterative refinement processes to augment their performance in
complex tasks.
