title: Knowledge Augmented Complex Problem Solving with Large Language Models:
    A Survey
summary: 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.
paper_authors._id: 681c4d5b5971460af345032a
paper_authors.name: Da Zheng
paper_authors._id: 681c4d5b5971460af345032b
paper_authors.name: Lun Du
paper_authors._id: 681c4d5b5971460af345032c
paper_authors.name: Junwei Su
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-14T13:01:56.639Z
paper_authors._id: 6534855b25a58aec659c1751
paper_authors.avatarUrl: /avatars/cd52b2183a7f39a75b37fd655fcab748.svg
paper_authors.fullname: Junwei Su
paper_authors.type: user
paper_authors.user: LittleTwon
paper_authors._id: 681c4d5b5971460af345032d
paper_authors.name: Yuchen Tian
paper_authors._id: 681c4d5b5971460af345032e
paper_authors.name: Yuqi Zhu
paper_authors._id: 681c4d5b5971460af345032f
paper_authors.name: Jintian Zhang
paper_authors._id: 681c4d5b5971460af3450330
paper_authors.name: Lanning Wei
paper_authors._id: 681c4d5b5971460af3450331
paper_authors.name: Ningyu Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-14T13:01:34.658Z
paper_authors._id: 620b3bbb0668e435407c8d0a
paper_authors.avatarUrl: /avatars/e0fccbb2577d76088e09f054c35cffbc.svg
paper_authors.fullname: Ningyu Zhang
paper_authors.type: user
paper_authors.user: Ningyu
paper_authors._id: 681c4d5b5971460af3450332
paper_authors.name: Huajun Chen
paper_title: Knowledge Augmented Complex Problem Solving with Large Language Models:
    A Survey
paper_summary: 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.
paper_ai_summary: LLMs address complex problem-solving by integrating human-like reasoning and computational power, but face challenges in multi-step reasoning, domain knowledge, and result verification.
paper_ai_keywords: Large Language Models LLMs Chain-of-Thought CoT reasoning knowledge augmentation software engineering mathematical reasoning data analysis scientific research multi-step reasoning domain knowledge integration result verification