通过自提问分解问题，提升大模型推理能力
SQuARE: Sequential Question Answering Reasoning Engine for Enhanced Chain-of-Thought in Large Language Models
- 让模型先生成并解答多个辅助问题，再处理主问题
- 在多个数据集上显著优于传统思维链和重述回答方法
- 适合需要深度推理的复杂问答场景
在自然语言处理快速发展的背景下，大语言模型面临日益复杂的推理挑战。传统的思维链提示虽有成效，但未能充分激发模型的推理潜力。本文提出SQuARE（序列问答推理引擎），一种新型提示技术，通过自我追问机制提升推理能力。该方法在思维链框架基础上，引导模型在回答主问题前生成并解决多个辅助问题，促进对主题多维度的深入探索。我们在Llama 3和GPT-4o模型上开展广泛评估，覆盖多个问答数据集，结果表明SQuARE显著优于传统思维链提示及现有重述-回应方法。通过系统性地分解问题，SQuARE有效增强了大模型在推理任务中的表现。代码已公开于https://github.com/IntelLabs/RAG-FiT/tree/square。
原文摘要 · Abstract (English)
In the rapidly evolving field of Natural Language Processing, Large Language Models (LLMs) are tasked with increasingly complex reasoning challenges. Traditional methods like chain-of-thought prompting have shown promise but often fall short in fully leveraging a model's reasoning capabilities. This paper introduces SQuARE (Sequential Question Answering Reasoning Engine), a novel prompting technique designed to improve reasoning through a self-interrogation paradigm. Building upon CoT frameworks, SQuARE prompts models to generate and resolve multiple auxiliary questions before tackling the main query, promoting a more thorough exploration of various aspects of a topic. Our expansive evaluations, conducted with Llama 3 and GPT-4o models across multiple question-answering datasets, demonstrate that SQuARE significantly surpasses traditional CoT prompts and existing rephrase-and-respond methods. By systematically decomposing queries, SQuARE advances LLM capabilities in reasoning tasks. The code is publicly available at https://github.com/IntelLabs/RAG-FiT/tree/square.
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