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：我们研究了如何利用基于云的大语言模型（LLMs）提升分布式用户常见问题的回答精度。我们的研究聚焦于用户提出涉及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。针对大语言模型在零样本提示下对独立问题回答准确率不理想的现状，我们提出利用自我一致性（Self-Consistency, SC）与思维链（Chain-of-Thought, CoT）技术改进分布式同义问题的处理方法。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不同的联邦同义问题分别称为SP-问题与DP-问题。我们将提出的方法命名为Fed-SP-SC与Fed-DP-CoT，这些方法无需复杂模型微调即可为所有用户查询生成显著更准确的答案。通过大量实验证明，我们提出的方法通过充分挖掘问题的同义性与答案的一致性，能够显著提升问题回答的准确率。