知識增強型大型語言模型在複雜問題解決中的應用：綜述

Knowledge Augmented Complex Problem Solving with Large Language Models: A Survey

May 6, 2025

作者: Da Zheng, Lun Du, Junwei Su, Yuchen Tian, Yuqi Zhu, Jintian Zhang, Lanning Wei, Ningyu Zhang, Huajun Chen

cs.AI

摘要

問題解決一直是推動人類在眾多領域進步的根本動力。隨著人工智慧的發展，大型語言模型（LLMs）已成為能夠應對跨領域複雜問題的強大工具。與傳統的計算系統不同，LLMs結合了原始計算能力與近似人類推理的能力，使其能夠生成解決方案、進行推論，甚至利用外部計算工具。然而，將LLMs應用於現實世界的問題解決面臨著重大挑戰，包括多步推理、領域知識整合和結果驗證。本調查探討了LLMs在複雜問題解決中的能力與限制，檢視了包括思維鏈（CoT）推理、知識增強以及各種基於LLM和工具的驗證技術。此外，我們強調了不同領域中的特定挑戰，如軟體工程、數學推理與證明、數據分析與建模以及科學研究。本文進一步討論了當前LLM解決方案的基本限制，並從多步推理、領域知識整合和結果驗證的角度探討了基於LLM的複雜問題解決的未來方向。

English

Problem-solving has been a fundamental driver of human progress in numerous domains. With advancements in artificial intelligence, Large Language Models (LLMs) have emerged as powerful tools capable of tackling complex problems across diverse domains. Unlike traditional computational systems, LLMs combine raw computational power with an approximation of human reasoning, allowing them to generate solutions, make inferences, and even leverage external computational tools. However, applying LLMs to real-world problem-solving presents significant challenges, including multi-step reasoning, domain knowledge integration, and result verification. This survey explores the capabilities and limitations of LLMs in complex problem-solving, examining techniques including Chain-of-Thought (CoT) reasoning, knowledge augmentation, and various LLM-based and tool-based verification techniques. Additionally, we highlight domain-specific challenges in various domains, such as software engineering, mathematical reasoning and proving, data analysis and modeling, and scientific research. The paper further discusses the fundamental limitations of the current LLM solutions and the future directions of LLM-based complex problems solving from the perspective of multi-step reasoning, domain knowledge integration and result verification.