Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.03418 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913823057772544 |
|---|---|
| author | Zheng, Da Du, Lun Su, Junwei Tian, Yuchen Zhu, Yuqi Zhang, Jintian Wei, Lanning Zhang, Ningyu Chen, Huajun |
| author_facet | Zheng, Da Du, Lun Su, Junwei Tian, Yuchen Zhu, Yuqi Zhang, Jintian Wei, Lanning Zhang, Ningyu Chen, Huajun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_03418 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Knowledge Augmented Complex Problem Solving with Large Language Models: A Survey Zheng, Da Du, Lun Su, Junwei Tian, Yuchen Zhu, Yuqi Zhang, Jintian Wei, Lanning Zhang, Ningyu Chen, Huajun Machine Learning 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. |
| title | Knowledge Augmented Complex Problem Solving with Large Language Models: A Survey |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.03418 |