Multimodal large language models and physics visual tasks: comparative analysis of performance and costs
Project Overview
The document explores the utilization of multimodal large language models (MLLMs) in the realm of physics education, emphasizing their effectiveness in tasks that necessitate visual interpretation. It assesses multiple models from leading providers, analyzing their accuracy, financial implications, and potential applications in tutoring, assessment, and feedback generation. The findings indicate significant disparities in model performance, which carry important considerations for educational institutions in selecting models that are both capable and cost-effective. While the study underscores the potential of MLLMs to enrich physics education by offering innovative learning tools, it also points out their limitations, particularly regarding the complexity of visual tasks. Overall, the document presents a balanced view of the opportunities and challenges posed by generative AI in educational settings, suggesting that while MLLMs can significantly enhance learning experiences, careful consideration is required in their adoption and implementation.
Key Applications
Multimodal large language models (MLLMs) for physics education
Context: Physics education, targeting instructors and students in undergraduate physics courses
Implementation: Evaluating models on standardized, image-based physics assessments to determine their effectiveness in tutoring and grading
Outcomes: Models showed varying performance, with some achieving accuracy comparable to high-performing students on conceptual physics tasks
Challenges: Limitations in handling complex visual representations and providing nuanced feedback; high variability in performance and cost.
Implementation Barriers
Technical Barrier
MLLMs often struggle with interpreting visual inputs, which are critical in physics education.
Proposed Solutions: Carefully designed prompting strategies to enhance model reliability and tailored evaluations to identify model weaknesses.
Financial and Equity Barrier
The cost of using high-performing MLLMs can be prohibitive for under-resourced institutions, potentially reinforcing existing technological divides in education.
Proposed Solutions: Evaluating cost-performance ratios to identify affordable models that still deliver adequate educational value, and ensuring that effective, lower-cost models are accessible to a broader range of learners and institutions.
Project Team
Giulia Polverini
Researcher
Bor Gregorcic
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Giulia Polverini, Bor Gregorcic
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