Training and Evaluating Language Models with Template-based Data Generation
Project Overview
The document explores the innovative use of generative AI, particularly through Template-based Data Generation (TDG) utilizing GPT-4, to tackle the challenge of limited high-quality datasets in education, specifically in grade school mathematics. By generating over 7 million synthetic math problems with verified solutions, the TemplateGSM dataset significantly enhances the availability of training data necessary for the development of advanced language models capable of performing complex reasoning tasks. This approach not only facilitates improved model training but also addresses the critical need for diverse and high-quality educational resources. The findings highlight the potential of generative AI to revolutionize educational practices by providing scalable solutions for content generation, ultimately leading to better learning outcomes and supporting a more data-driven approach to educational technology.
Key Applications
Template-based Data Generation (TDG)
Context: Educational context for grade school math, targeting students and language models.
Implementation: Utilized GPT-4 to generate parameterized templates for math problems, followed by simultaneous generation and verification of Q&A pairs.
Outcomes: Creation of TemplateGSM dataset with over 7 million validated math problems, enhancing data availability for training LLMs.
Challenges: Limited existing datasets for complex reasoning in math, ensuring the quality and correctness of generated problems.
Implementation Barriers
Data Scarcity
The lack of large-scale, high-quality datasets necessary for training models capable of sophisticated mathematical reasoning.
Proposed Solutions: Use Template-based Data Generation (TDG) to create a large synthetic dataset addressing the scarcity issue.
Quality Assurance
Ensuring that generated problems and solutions are correct and reliable.
Proposed Solutions: Implement a verification process using code execution and LLM checks to validate problem-solution pairs.
Project Team
Yifan Zhang
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Yifan Zhang
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