知识增强型大型语言模型在复杂问题解决中的应用：综述

摘要

问题解决一直是推动人类在众多领域进步的根本动力。随着人工智能技术的进步，大型语言模型（LLMs）已成为能够跨多个领域应对复杂问题的强大工具。与传统的计算系统不同，LLMs将原始计算能力与近似人类推理相结合，使其能够生成解决方案、进行推理，甚至利用外部计算工具。然而，将LLMs应用于现实世界的问题解决面临诸多挑战，包括多步推理、领域知识整合以及结果验证。本综述探讨了LLMs在复杂问题解决中的能力与局限，考察了包括思维链（CoT）推理、知识增强以及多种基于LLM和工具的验证技术。此外，我们强调了软件工程、数学推理与证明、数据分析与建模以及科学研究等不同领域中的特定挑战。本文还从多步推理、领域知识整合和结果验证的角度，讨论了当前LLM解决方案的基本局限以及基于LLM的复杂问题解决的未来方向。

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.

知识增强型大型语言模型在复杂问题解决中的应用：综述

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

摘要

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