Paper Summary
Title: Gemini Pro Defeated by GPT-4V: Evidence from Education
Source: arXiv
Authors: Gyeong-Geon Lee et al.
Published Date: 2023-12-27
Podcast Transcript
Hello, and welcome to Paper-to-Podcast.
Today's episode brings us to the thrilling world of classroom grading, where artificial intelligence systems are throwing down the gauntlet in a showdown that's more gripping than a robot arm at a science fair! We're diving into a paper that's so fresh off the press, it might as well have ink on its digital fingers. Published on December 27th, 2023, by Gyeong-Geon Lee and colleagues, this study is titled "Gemini Pro Defeated by GPT-4V: Evidence from Education."
Now, if you've ever gazed at a student's science drawing and thought, "What on Earth is that?" you're not alone. But fear not, because GPT-4V is here to save the day! When we look at the findings, it's like watching a heavyweight championship. GPT-4V soared through the scoring with the grace of a ballet-dancing eagle, achieving a mean accuracy score of 0.48 across all tasks. Gemini Pro, on the other hand, limped along with a 0.30 score on the single task it managed to complete. Yikes! That's a 60% difference in accuracy, making GPT-4V the undisputed champion of classroom grading.
And it's not just about the big picture. When it comes to the nitty-gritty details in the drawings, GPT-4V turned into Sherlock Holmes with a magnifying glass, while Gemini Pro seemed to mistake a science diagram for abstract art. Even when the researchers tried to give Gemini Pro a leg-up by simplifying the images, it still couldn't match the superhero-like scoring prowess of GPT-4V.
The methods? They were as meticulous as a cat grooming its fur. The researchers pitted Gemini Pro against GPT-4V using visual question answering techniques on a dataset of student-drawn scientific models. They employed the Notation-Enhanced Rubrics for Image Feedback, or NERIF, prompting method, which is like giving the AIs a cheat sheet, but for science.
Three experiments were conducted, including a qualitative analysis that was more detailed than a high schooler's excuses for not doing homework. They even modified the prompt design for Gemini Pro, which could only handle one image at a time—bless its digital heart.
The strengths of this paper are as clear as the "Eureka!" moment when you finally understand algebra. The head-to-head comparison of these two AI models in the educational arena was as rigorous as a boot camp workout. The use of the NERIF method ensured that the AIs were well-prepared, like students on exam day after pulling an all-nighter.
But no study is perfect, right? There are limitations, such as the fact that the research only tested the AIs' ability to score drawings. It's like judging a talent show based solely on juggling skills. And let's not forget that the NERIF method might have its biases, like a proud parent at a talent show.
The potential applications of this research are as exciting as a field trip to NASA. Imagine GPT-4V being used to grade and give feedback on scientific diagrams, turning the tedious task of grading into a breeze for teachers and providing instant feedback to students. The possibilities for AI in education are as vast as the library of Alexandria—before it got burned down, of course.
So, if you want to delve deeper into the epic battle between Gemini Pro and GPT-4V, and perhaps place your bets on the next AI grading gladiator, you can find this paper and more on the paper2podcast.com website.
Supporting Analysis
Sure thing! The paper revealed that when it came to scoring science drawings made by students, GPT-4V totally rocked it compared to Gemini Pro. It's like GPT-4V was a sharp-eyed eagle, nailing the accuracy with a score of 0.48 (mean) across all tasks, while Gemini Pro kind of squinted and guessed, scoring only 0.30 on the one task it could complete. Talk about a clear winner! But here's the kicker, GPT-4V didn't just win by a little; its accuracy was a whopping 60% better than Gemini Pro's. And when it came to understanding the fine print in pictures, GPT-4V was like a detective with a magnifying glass, catching all the tiny details, while Gemini Pro was more like, "Hmm, looks like a poster to me," even when it wasn't. The study also tried giving Gemini Pro a little nudge by simplifying the images, but even though it did a bit better, it was still like comparing a superhero to a sidekick in terms of scoring those student masterpieces.
The research compared two advanced AI models, Gemini Pro and GPT-4V, focusing on their ability to automatically score student-drawn scientific models in education using visual question answering (VQA) techniques. The study utilized a dataset from student-drawn scientific models and employed the Notation-Enhanced Rubrics for Image Feedback (NERIF) prompting method. The researchers conducted three experiments. In the first, they assessed the classification performance of both models by providing them with text-based rubrics and images of student work. The second experiment involved a qualitative analysis, examining each model's ability to process fine-grained texts and overall image classification. The third experiment attempted to improve Gemini Pro's performance by adapting NERIF, including resizing input images. For the prompt design, they employed the NERIF method, which includes defining a role for the AI, explaining the task, providing problem context, and offering examples for few-shot learning. This setup was slightly modified to accommodate Gemini Pro's limitation of processing only one image at a time. The research used quantitative measures such as scoring accuracy and Quadratic Weighted Kappa to compare the models' scoring performance.
The most compelling aspect of this research is the direct comparison of two advanced AI models, Gemini Pro and GPT-4V, specifically in the context of educational settings. The study's focus on the models' ability to score student-drawn science models using visual question answering techniques is particularly noteworthy as it pushes the boundaries of AI application in education. The researchers employed a combination of quantitative and qualitative analyses, which allowed for a robust understanding of the models' performance. The researchers followed best practices by using the NERIF (Notation-Enhanced Rubrics for Image Feedback) prompting method, which is designed to improve AI performance on such tasks. This structured method consists of providing essential components, validation cases, and iterative revisions to enhance the machine’s performance, ensuring that the study's approach was systematic and thorough. They also adapted their methods by simplifying the input for Gemini Pro when it struggled with more complex inputs, demonstrating flexibility and an understanding of the limitations of the models being studied. Overall, the study's methodical approach and the use of a well-defined prompting strategy underscore its commitment to rigor and innovation in AI research within education.
The research paper's comparison between Gemini Pro and GPT-4V in an educational setting, specifically in scoring student-drawn models, may face several potential limitations. Firstly, the study's focus on visual question answering (VQA) techniques primarily tests the AI's ability to process and interpret images and text, which may not fully assess the models' performance in other educational tasks or contexts. Secondly, the use of the NERIF (Notation-Enhanced Rubrics for Image Feedback) prompting method, while innovative, may have inherent biases based on the design and implementation of the rubrics, potentially influencing the AI's scoring performance. Another limitation could be the sample size and diversity of the student-drawn models used in the study. If the dataset lacks variety or is not representative of a wider student population, the findings may not be generalizable to different educational settings or subjects. Furthermore, the rapid pace of AI development means that both Gemini Pro and GPT-4V are continuously evolving, with updates that could render the study's findings obsolete shortly after publication. Finally, the study's reliance on quantitative analysis may overlook qualitative aspects of educational tasks, such as the interpretative and subjective nature of some student responses.
The research has potential applications in the field of education, specifically in the enhancement of multimodal learning and assessment tools. The demonstrated capabilities of GPT-4V could be utilized in various educational software and platforms to automatically grade and provide feedback on student-submitted visual materials, such as scientific diagrams or drawn models. This could drastically reduce the workload of educators by automating part of the grading process while providing immediate, consistent feedback to students. Moreover, the findings could influence the development of more sophisticated AI-driven educational tools that can process and analyze multimodal data, including text and images. Such tools could support personalized learning experiences by adapting to students' individual needs based on their responses and performance. The research could also spearhead further exploration into the development of AI that can handle complex tasks involving the integration of visual and textual information, thereby advancing the field of AI in educational technology.