MathLearner: A Large Language Model Agent Framework for Learning to Solve Mathematical Problems
Project Overview
The document explores the application of generative AI in education, specifically through the MathLearner framework, which aims to enhance the mathematical reasoning skills of large language models (LLMs) by mimicking human learning processes. MathLearner provides a structured approach for LLMs to learn from examples, memorize problem-solving techniques, and utilize prior knowledge to tackle mathematical challenges. This innovative framework demonstrates significant improvements in accuracy over traditional educational methods, positioning it as an effective personalized learning tool that addresses disparities in educational resources. However, the document also highlights certain limitations, such as the challenges in accurately simulating human learning, alongside the need for advancements in feature generalization and the format of solutions. Overall, the findings underscore the potential of generative AI to transform educational practices, while also recognizing the areas that require further development to maximize its effectiveness in fostering student learning.
Key Applications
MathLearner: A framework for learning to solve mathematical problems
Context: Educational context aimed at students needing personalized learning support in mathematics
Implementation: The framework operates in three stages: learning from examples, memorizing solving methods, and recalling previous knowledge to solve new problems.
Outcomes: Improves global accuracy by 20.96% over baseline methods and resolves 17.54% of previously unsolvable problems.
Challenges: Limited simulation of human learning processes, ambiguity in defining features, and difficulty in generating suitable formats for solutions.
Implementation Barriers
Technical Barrier
Incomplete simulation of human learning processes in LLMs, which affects their ability to learn and apply knowledge dynamically. Limitations in the format of modified solutions, as some problems may not be effectively solvable through programming alone.
Proposed Solutions: Future work can focus on empowering LLMs to perform real-time updates of knowledge, fine-tuning them to utilize all possible features, and using both programming and natural language to formulate solutions.
Conceptual Barrier
Ambiguity in the definition of features, making it challenging to categorize problems and derive suitable solutions.
Proposed Solutions: Research should aim to design a standardized language for features and develop techniques for LLMs to produce consistent feature representations.
Project Team
Wenbei Xie
Researcher
Donglin Liu
Researcher
Haoran Yan
Researcher
Wenjie Wu
Researcher
Zongyang Liu
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Wenbei Xie, Donglin Liu, Haoran Yan, Wenjie Wu, Zongyang Liu
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai