用智能向量化辅助学习数学建模与MATLAB应用
VectorizationLLM: Smart Vectorization Based AI Assistant
- 基于RAG架构构建教学助手，结合课堂笔记提供讲解
- 支持代码、文本、图像多形式示例，不直接给答案
- 适合工程类学生在计算分析课中深化向量与函数理解
VectorizationLLM 是基于谷歌开源大模型的专用语言模型，专为纽约理工学院奥尔德韦斯特伯里分校电子与计算机工程技术系的课程 CTEC 247：应用计算分析 II 设计。该模型旨在帮助学生掌握智能向量化、时频分析、分段函数、傅里叶分析及微分方程等核心概念，并在 MATLAB 环境中实践。作为教学辅助工具，模型通过检索增强生成（RAG）知识库和系统提示架构，提供基于课堂笔记的详细概念解释与多模态示例（代码、文本、图像），但不会直接给出问题答案，以促进主动学习。
原文摘要 · Abstract (English)
VectorizationLLM is a specialized Large Language Model based on Google open-weight LLMs. The model is designed to assist students to learn smart vectorization, time/wave vector analysis, piecewise functions, Fourier analysis, and differential equations in MATLAB. The course application is CTEC 247: Applied Computational Analysis II by the Department of Electrical & Computer Engineering Technology at New York Institute of Technology Old Westbury. The LLM model is designed to be an instructive assistant, providing detailed explanations of concepts with examples from in-class notes without providing direct answers to questions. The model is designed with a RAG (Retrieval Augmented Generation) knowledge base and system prompt architecture. Examples in both code, text, and images are provided in the LLM responses.
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