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.

本文介绍了 DiversiGATE，一个统一的框架，它 consolodates 了 LLM 验证的各种方法，并且提出了一种符合 DiversiGATE 框架的新型 SelfLearner 模型，可以从自己的输出中学习并不断改善性能。通过实验，表明 SelfLearner 的有效性，对 GSM8K 基准测试的改进达到了 54.8％-> 61.8％。