Paper Summary
Title: Testing GPT-4 with Wolfram Alpha and Code Interpreter plug-ins on math and science problems
Source: arXiv
Authors: Ernest Davis, Scott Aaronson
Published Date: 2023-08-11
Podcast Transcript
Hello, and welcome to Paper-to-Podcast. Today, we're diving into the world of artificial intelligence, where the AI version of Homer Simpson is attempting to solve science and math problems. It's like The Simpsons meets Good Will Hunting, only with more silicon!
Ernest Davis and Scott Aaronson decided to run a sophisticated language model, GPT-4, through the academic wringer. They armed it with the Wolfram Alpha and Code Interpreter plug-ins and threw 105 original math and science problems at it.
Our AI student showed some impressive skills, and when partnered with the plugins, it outshone its earlier version and any AI that existed a year ago. However, it wasn't all smooth sailing. Like a freshman who skipped a few too many lectures, this AI had its hiccups. It nailed problems that needed a single formula but tripped over problems that required spatial visualization or combining different calculations. And let's just say, dealing with very big or very small numbers wasn't this AI's strong suit.
In terms of grading, this AI would be a solid C undergraduate student. It had some ability to spot when the answers were complete gibberish, but not reliably. So, it seems our AI 'student' still has some late-night cramming to do before tackling those college-level calculation problems.
Now, let's talk about the methods of this academic gauntlet. Davis and Aaronson used the Wolfram Alpha and Code Interpreter plugins to test GPT-4 on a range of problems replicating high school and college-level challenges. These problems were categorized into three sets: Arbitrary Numerical, Calculation-Free, and Motivated Numerical. They also ensured each problem had its own chat session to avoid any cross-contamination of answers.
The strengths of the research lay in the unique testing approach and detailed analysis of the AI systems' responses. Imagine an overly meticulous teacher marking an exam paper, and you'll get the idea. This rigorous approach allowed them to pinpoint the AI's strengths, weaknesses, and patterns in problem-solving abilities.
However, like that one friend who's fun at parties but doesn't always make the best decisions, the research had its limitations. The test set was small and idiosyncratic, making it hard to draw statistically valid conclusions. Also, the methodology, while providing detailed analysis, would be quite expensive to scale up.
The potential applications of this research are primarily educational. As GPT-4 or similar models improve, they could become powerful study aids for students. Imagine having a personal AI tutor to help solve complex problems, understand difficult concepts, and check homework. It could also be a helpful tool for educators to create and grade assignments or provide additional support to students. Beyond education, this research could have applications in fields that involve problem-solving, such as engineering, data analysis, and scientific research.
So, whether you're a fan of AI, a math or science enthusiast, or just someone who enjoys hearing about complex models being put through their paces, this research offers a fascinating look into the strengths and weaknesses of GPT-4 as it grapples with math and science problems.
And, who knows, maybe one day, we'll be able to say: "Hey GPT-4, do my homework!" and it will confidently reply: "Of course, I can do that, and by the way, you made an error in your second equation."
That's it for this episode of Paper-to-Podcast. You can find this paper and more on the paper2podcast.com website.
Supporting Analysis
In a mix of a game show and a college exam, GPT-4, a giant language model, was tested using the Wolfram Alpha and Code Interpreter plug-ins on 105 original problems in science and math. This AI student showed some impressive skills but also had its "doh" moments. When paired with the plugins, GPT-4 performed better than its solo self or any AI that existed a year ago. However, it still had some hiccups and occasionally gave wrong answers or no answers at all. If we were to grade it, it would be at the level of a 'meh' undergraduate student. It aced problems that involved a single formula, but stumbled on problems that required spatial visualization or combining different calculations. Also, dealing with very big or very small numbers was a bit tough for it. It had some ability to spot when the answers were meaningless, but not reliably. So, while it shows promise, this AI 'student' still has some studying to do before it can reliably solve college-level calculation problems.
In this study, the researchers put the artificial intelligence model GPT-4 to the test. They used two plugins, Wolfram Alpha and Code Interpreter, and evaluated the system's performance on 105 science and math problems. These problems were designed to replicate high school and college-level challenges, and the test was conducted over several months. The researchers categorized the problems into three sets: Arbitrary Numerical, Calculation-Free, and Motivated Numerical. The first set required numerical or vector answers, the second set called for discrete answers, and the third set involved multiple types of calculations. The researchers manually reviewed the AI's responses on each problem for in-depth analysis. They also created a new chat session for each problem to avoid cross-question contamination.
The most compelling aspects of this research are the unique testing approach and the detailed analysis of the AI systems' responses. The researchers adopted a rigorous and meticulous methodology, which involved creating an idiosyncratic test set and examining the entire output. This meticulous approach allowed them to pinpoint strengths, weaknesses, and patterns in the AI systems' problem-solving abilities. They also took care to ensure that the test problems were expert-crafted and covered a range of scientific and mathematical topics. Furthermore, they were transparent about the limitations of their testing approach, acknowledging the potential interpretive issues that could arise from their anthropomorphic categorization of errors. Overall, their commitment to thoroughness, rigor, and transparency sets a good example for future research in this area.
The research is limited by the small and idiosyncratic nature of the test set used. This makes it hard to draw statistically valid conclusions from the results. The research also heavily relies on the explanations generated by the AI systems in their output, which can be risky as the AI may just be finding the most likely sequence of tokens to fit the explanation slot, rather than genuinely understanding the problem. Additionally, the researchers did not experiment with few-shot prompts or “chain of thought” prompts, which could potentially have affected the results. The methodology used, while providing detailed analysis, is labor-intensive and would be expensive to scale to a larger test set. Lastly, the research only looks at GPT-4's performance in the context of math and science problems, which may not reflect its performance in other areas.
The potential applications of this research are primarily educational. As GPT-4 or similar models are further improved and their ability to interface with plugins like Wolfram Alpha and Code Interpreter becomes more seamless, they could become powerful tools for students studying high school and college-level math and science. Students could use these AI systems as study aids to help them solve complex problems, understand difficult concepts, and check their homework. Furthermore, educators could use these models to create and grade assignments or provide additional support to students who need more practice. Beyond education, this research could also have applications in any field that involves problem-solving, such as engineering, data analysis, and scientific research, where AI could assist humans in performing calculations or interpreting data.