Federated Prompting and Chain-of-Thought Reasoning for Improving LLMs Answering

• URL: http://arxiv.org/abs/2304.13911v2
• Date: Fri, 30 Jun 2023 13:21:36 GMT
• Title: Federated Prompting and Chain-of-Thought Reasoning for Improving LLMs Answering
• Authors: Xiangyang Liu, Tianqi Pang, Chenyou Fan
• Abstract summary: 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. We propose to improve the distributed synonymous questions using Self-Consistency (SC) and Chain-of-Thought (CoT) techniques.
• Score: 13.735277588793997
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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