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

• URL: http://arxiv.org/abs/2505.03418v1
• Date: Tue, 06 May 2025 10:53:58 GMT
• Title: Knowledge Augmented Complex Problem Solving with Large Language Models: A Survey
• Authors: Da Zheng, Lun Du, Junwei Su, Yuchen Tian, Yuqi Zhu, Jintian Zhang, Lanning Wei, Ningyu Zhang, Huajun Chen,
• Abstract summary: Large Language Models (LLMs) have emerged as powerful tools capable of tackling complex problems across diverse domains.<n>Applying LLMs to real-world problem-solving presents significant challenges, including multi-step reasoning, domain knowledge integration, and result verification.
• Score: 48.53273952814492
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Computational Thinking Reasoning in Large Language Models [69.28428524878885]
  Computational Thinking Model (CTM) is a novel framework that incorporates computational thinking paradigms into large language models (LLMs)<n>Live code execution is seamlessly integrated into the reasoning process, allowing CTM to think by computing.<n>CTM outperforms conventional reasoning models and tool-augmented baselines in terms of accuracy, interpretability, and generalizability.
  arXiv  Detail & Related papers  (2025-06-03T09:11:15Z)
• ModelingAgent: Bridging LLMs and Mathematical Modeling for Real-World Challenges [72.19809898215857]
  We introduce ModelingBench, a novel benchmark featuring real-world-inspired, open-ended problems from math modeling competitions across diverse domains.<n>These tasks require translating natural language into formal mathematical formulations, applying appropriate tools, and producing structured, defensible reports.<n>We also present ModelingAgent, a multi-agent framework that coordinates tool use, supports structured, creative solutions, and generates well-grounded, creative solutions.
  arXiv  Detail & Related papers  (2025-05-21T03:33:23Z)
• A Survey on Mathematical Reasoning and Optimization with Large Language Models [0.5439020425819]
  Recent advancements in Large Language Models (LLMs) have significantly improved AI-driven mathematical reasoning, theorem proving, and optimization techniques.<n>This survey explores the evolution of mathematical problem-solving in AI, from early statistical learning approaches to modern deep learning and transformer-based methodologies.
  arXiv  Detail & Related papers  (2025-03-22T10:49:32Z)
• MathOdyssey: Benchmarking Mathematical Problem-Solving Skills in Large Language Models Using Odyssey Math Data [20.31528845718877]
  Large language models (LLMs) have significantly advanced natural language understanding and demonstrated strong problem-solving abilities. This paper investigates the mathematical problem-solving capabilities of LLMs using the newly developed "MathOdyssey" dataset.
  arXiv  Detail & Related papers  (2024-06-26T13:02:35Z)
• Multi-step Inference over Unstructured Data [2.169874047093392]
  High-stakes decision-making tasks in fields such as medical, legal and finance require a level of precision, comprehensiveness, and logical consistency. We have developed a neuro-symbolic AI platform to tackle these problems. The platform integrates fine-tuned LLMs for knowledge extraction and alignment with a robust symbolic reasoning engine.
  arXiv  Detail & Related papers  (2024-06-26T00:00:45Z)
• Puzzle Solving using Reasoning of Large Language Models: A Survey [1.9939549451457024]
  This survey examines the capabilities of Large Language Models (LLMs) in puzzle solving. Our findings highlight the disparity between LLM capabilities and human-like reasoning. The survey underscores the necessity for novel strategies and richer datasets to advance LLMs' puzzle-solving proficiency.
  arXiv  Detail & Related papers  (2024-02-17T14:19:38Z)
• Large Language Models for Mathematical Reasoning: Progresses and Challenges [15.925641169201747]
  Large Language Models (LLMs) are geared towards the automated resolution of mathematical problems. This survey endeavors to address four pivotal dimensions. It provides a holistic perspective on the current state, accomplishments, and future challenges in this rapidly evolving field.
  arXiv  Detail & Related papers  (2024-01-31T20:26:32Z)
• Evaluating LLMs' Mathematical and Coding Competency through Ontology-guided Interventions [47.83142414018448]
  We focus on two popular reasoning tasks: arithmetic reasoning and code generation. We introduce (i) a general ontology of perturbations for math and coding questions, (ii) a semi-automatic method to apply these perturbations, and (iii) two datasets. We show a significant performance drop across all the models against perturbed questions.
  arXiv  Detail & Related papers  (2024-01-17T18:13:07Z)
• MacGyver: Are Large Language Models Creative Problem Solvers? [87.70522322728581]
  We explore the creative problem-solving capabilities of modern LLMs in a novel constrained setting.<n>We create MACGYVER, an automatically generated dataset consisting of over 1,600 real-world problems.<n>We present our collection to both LLMs and humans to compare and contrast their problem-solving abilities.
  arXiv  Detail & Related papers  (2023-11-16T08:52:27Z)
• Knowledge Crosswords: Geometric Knowledge Reasoning with Large Language Models [49.23348672822087]
  We propose Knowledge Crosswords, a benchmark consisting of incomplete knowledge networks bounded by structured factual constraints. The novel setting of geometric knowledge reasoning necessitates new LM abilities beyond existing atomic/linear multi-hop QA. We conduct extensive experiments to evaluate existing LLMs and approaches on Knowledge Crosswords.
  arXiv  Detail & Related papers  (2023-10-02T15:43:53Z)
• SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models [70.5763210869525]
  We introduce an expansive benchmark suite SciBench for Large Language Model (LLM) SciBench contains a dataset featuring a range of collegiate-level scientific problems from mathematics, chemistry, and physics domains. The results reveal that the current LLMs fall short of delivering satisfactory performance, with the best overall score of merely 43.22%.
  arXiv  Detail & Related papers  (2023-07-20T07:01:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.