DualSchool: How Reliable are LLMs for Optimization Education?
Project Overview
The document explores the application of Large Language Models (LLMs) in education, particularly through a framework known as DUALSCHOOL, aimed at optimizing learning in areas such as linear programming. It emphasizes the potential of LLMs to articulate educational concepts and procedures; however, it also reveals significant challenges in their ability to execute complex tasks, such as converting primal forms to dual forms in optimization problems. The findings highlight the limitations of LLMs in educational contexts, demonstrating that while they can produce outputs that seem plausible, these outputs frequently lack accuracy and reliability, making them unsuitable for educational purposes. Consequently, the document calls attention to the need for caution when integrating generative AI into educational frameworks, as the technology, despite its promise, may not yet meet the rigorous standards required for effective teaching and learning.
Key Applications
DUALSCHOOL framework for Primal-to-Dual Conversion (P2DC)
Context: Used in introductory optimization courses for students learning linear programming.
Implementation: A framework that generates and verifies P2DC instances using automatic symbolic dualization and Canonical Graph Edit Distance for evaluation.
Outcomes: Provides a comprehensive evaluation of LLMs' ability to perform P2DC and related tasks, revealing the limitations of LLMs in accuracy despite high-quality textual outputs.
Challenges: LLMs often produce incorrect duals despite understanding the procedure; they may generate plausible outputs that fail in correctness.
Implementation Barriers
Technical Challenge
LLMs can articulate the procedure for tasks like P2DC but often fail to execute them correctly, leading to misconceptions about their reliability.
Proposed Solutions: Educators need to communicate the limitations of LLMs to students and provide additional support for understanding.
Project Team
Michael Klamkin
Researcher
Arnaud Deza
Researcher
Sikai Cheng
Researcher
Haoruo Zhao
Researcher
Pascal Van Hentenryck
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Michael Klamkin, Arnaud Deza, Sikai Cheng, Haoruo Zhao, Pascal Van Hentenryck
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