  In this paper, we introduce DiversiGATE, a unified framework that
consolidates diverse methodologies for LLM verification. The proposed framework
comprises two main components: Diversification and Aggregation which provide a
holistic perspective on existing verification approaches, such as
Self-Consistency, Math Prompter and WebGPT. Furthermore, we propose a novel
`SelfLearner' model that conforms to the DiversiGATE framework which can learn
from its own outputs and refine its performance over time, leading to improved
accuracy. To evaluate the effectiveness of SelfLearner, we conducted a rigorous
series of experiments, including tests on synthetic data as well as on popular
arithmetic reasoning benchmarks such as GSM8K. Our results demonstrate that our
approach outperforms traditional LLMs, achieving a considerable 54.8% -> 61.8%
improvement on the GSM8K benchmark.
