We investigate how to enhance answer precision in frequently asked questions
posed by distributed users using cloud-based Large Language Models (LLMs). Our
study focuses on a typical situations where users ask similar queries that
involve identical mathematical reasoning steps and problem-solving procedures.
Due to the unsatisfactory accuracy of LLMs' zero-shot prompting with standalone
questions, we propose to improve the distributed synonymous questions using
Self-Consistency (SC) and Chain-of-Thought (CoT) techniques. Specifically, we
first retrieve synonymous questions from a crowd-sourced database and create a
federated question pool. We call these federated synonymous questions with the
same or different parameters SP-questions or DP-questions, respectively. We
refer to our methods as Fed-SP-SC and Fed-DP-CoT, which can generate
significantly more accurate answers for all user queries without requiring
sophisticated model-tuning. Through extensive experiments, we demonstrate that
our proposed methods can significantly enhance question accuracy by fully
exploring the synonymous nature of the questions and the consistency of the
answers.

通过使用自我一致性和思维链技术来改善云端大型语言模型中的分布式同义问题，可以显着提高所有用户查询的准确性。