SQuARE：用于增强大型语言模型思维链的序列问答推理引擎
SQuARE: Sequential Question Answering Reasoning Engine for Enhanced Chain-of-Thought in Large Language Models
AI总结：
针对大型语言模型推理能力未被充分利用的问题，提出新型提示技术SQuARE，通过自我询问生成并解决辅助问题分解主查询，在多个数据集上显著优于传统思维链提示，提升推理任务性能。
摘要：
在快速发展的自然语言处理领域，大型语言模型（LLMs）面临着日益复杂的推理挑战。思维链提示等传统方法虽有前景，但往往无法充分利用模型的推理能力。本文介绍SQuARE（序列问答推理引擎），这是一种通过自我询问范式提升推理能力的新型提示技术。基于CoT框架，SQuARE提示模型在处理主查询前生成并解决多个辅助问题，促进对主题各方面的更深入探索。我们使用Llama 3和GPT-4o模型在多个问答数据集上进行的广泛评估表明，SQuARE显著优于传统CoT提示和现有重述-响应方法。通过系统地分解查询，SQuARE提升了LLM在推理任务中的能力。代码公开于https://github.com/IntelLabs/RAG-FiT/tree/square。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
