Evaluating Mathematical Reasoning Across Large Language Models: A Fine-Grained Approach
Project Overview
The document examines the role of generative AI in education, specifically focusing on the evaluation of mathematical reasoning capabilities in various Large Language Models (LLMs). It highlights the performance of leading models, including OpenAI's GPT-4o and DeepSeek-R1, across multiple benchmarks, revealing their strengths in processing complex mathematical problems while also addressing their weaknesses. Key applications of these LLMs in educational settings include personalized learning, automated tutoring, and enhancing student engagement through interactive problem-solving experiences. The findings underscore the need for continuous improvement in LLM capabilities to better support educational outcomes, particularly in refining their mathematical reasoning skills. Additionally, the document emphasizes the importance of tackling computational demands and generalization challenges that arise during the application of these models in educational contexts. Overall, the use of generative AI in education presents significant potential for transforming learning experiences, but it also necessitates ongoing research and development to maximize effectiveness and accessibility.
Key Applications
Evaluation of mathematical reasoning in LLMs
Context: Educational context focusing on advanced mathematical problem-solving tasks, targeting researchers and developers in AI and education.
Implementation: Systematic evaluation using benchmark datasets (MATH, GSM8K, MMLU) and a zero-shot prompting strategy to assess LLMs' reasoning capabilities.
Outcomes: Comparative analysis revealing LLMs' strengths and weaknesses in mathematical reasoning, with implications for future AI model development.
Challenges: High computational demands, performance inconsistencies in specialized tasks, and limitations in generalization across diverse mathematical domains.
Implementation Barriers
Technical Barrier
High computational demands for running advanced LLMs, making large-scale implementation costly.
Proposed Solutions: Exploration of more efficient model architectures, such as the Mixture-of-Experts (MoE) approach to reduce computational overhead.
Performance Barrier
LLMs struggle with specialized tasks, showing inconsistencies in performance and reasoning capabilities.
Proposed Solutions: Development of hybrid reasoning frameworks combining reinforcement learning and structured step-by-step inference methods.
Project Team
Afrar Jahin
Researcher
Arif Hassan Zidan
Researcher
Wei Zhang
Researcher
Yu Bao
Researcher
Tianming Liu
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Afrar Jahin, Arif Hassan Zidan, Wei Zhang, Yu Bao, Tianming 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