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.

翻译：我们探究如何利用云端大语言模型（LLMs）提升分布式用户常见问题的回答精度。本研究聚焦于用户提出相同数学推理步骤与问题求解流程的相似查询这一典型场景。针对LLMs独立问题零样本提示准确率不足的问题，我们提出采用自一致性（SC）与思维链（CoT）技术改进分布式同义问题的回答质量。具体而言，我们首先从众包数据库中检索同义问题并构建联邦问题池。我们将这些参数相同或相异的联邦同义问题分别称为SP问题和DP问题。我们提出的Fed-SP-SC与Fed-DP-CoT方法无需复杂模型微调，即可为所有用户查询生成显著更精准的答案。通过大量实验证明，本方法通过充分挖掘问题的同义特性与答案的一致性，能够有效提升问题回答准确率。