Confucius3-Math: A Lightweight High-Performance Reasoning LLM for Chinese K-12 Mathematics Learning
Project Overview
The document discusses the introduction of Confucius3-Math, an innovative open-source large language model (LLM) tailored for K-12 mathematics education in China, featuring 14 billion parameters and leveraging reinforcement learning to excel in mathematical reasoning tasks. This model prioritizes affordability and accessibility, showing considerable improvements in performance compared to larger models while keeping operational costs low. Key applications of Confucius3-Math include personalized tutoring and support for students, enabling them to grasp complex mathematical concepts effectively. Despite its advancements, the document highlights ongoing challenges such as ensuring the model's accuracy and addressing educational inequality, which are critical to fully realizing the potential of generative AI in educational contexts. Overall, the findings demonstrate that generative AI, particularly through the development of targeted models like Confucius3-Math, can significantly enhance learning outcomes in mathematics, although careful consideration of equity and precision remains essential for widespread adoption.
Key Applications
Confucius3-Math - a lightweight reasoning LLM for K-12 mathematics.
Context: Targeted at Chinese K-12 students and educators, focusing on mathematics learning.
Implementation: Developed using a post-training reinforcement learning approach on a consumer-grade GPU, achieving high performance at low cost.
Outcomes: Achieved state-of-the-art performance on various K-12 math benchmarks, demonstrating feasibility for low-cost education solutions.
Challenges: Challenges include ensuring model accuracy and accessibility for students from low-income backgrounds.
Implementation Barriers
Technical Barrier
Ensuring high accuracy is crucial as incorrect answers can negatively impact student learning.
Proposed Solutions: Focus on model adaptation to improve performance on K-12 tasks.
Economic Barrier
High-performance LLMs are expensive to build and deploy, creating a digital divide among students.
Proposed Solutions: Develop low-cost models like Confucius3-Math to democratize access to educational resources.
Project Team
Lixin Wu
Researcher
Na Cai
Researcher
Qiao Cheng
Researcher
Jiachen Wang
Researcher
Yitao Duan
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Lixin Wu, Na Cai, Qiao Cheng, Jiachen Wang, Yitao Duan
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