Knowledge Augmented Complex Problem Solving with Large Language Models:
  A Survey
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.
681c4d5b5971460af345032a
Da Zheng
681c4d5b5971460af345032b
Lun Du
681c4d5b5971460af345032c
Junwei Su
admin_assigned
2025-05-14T13:01:56.639Z
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Junwei Su
user
LittleTwon
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Yuchen Tian
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Yuqi Zhu
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2025-06-25T13:32:58.911Z
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Yuqi Zhu
user
Yukirsh
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Jintian Zhang
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Lanning Wei
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Ningyu Zhang
admin_assigned
2025-05-14T13:01:34.658Z
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/avatars/e0fccbb2577d76088e09f054c35cffbc.svg
Ningyu Zhang
user
Ningyu
681c4d5b5971460af3450332
Huajun Chen
Knowledge Augmented Complex Problem Solving with Large Language Models:
  A Survey
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.
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.
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