ΑΙhub.org
 

Why ChatGPT struggles with math


by
07 November 2024



share this:

Have you ever tried to use an AI tool like ChatGPT to do some math and found it doesn’t always add up? It turns out there’s a reason for that.

As large language models (LLMs) like OpenAI’s ChatGPT become more ubiquitous, people increasingly rely on them for work and research assistance. Yuntian Deng, assistant professor at the David R. Cheriton School of Computer Science, discusses some of the challenges in LLMs’ reasoning capabilities, particularly in math, and explores the implications of using these models to aid problem-solving.

What flaw did you discover in ChatGPT’s ability to do math?

As I explained in a recent post on X, the latest reasoning variant of ChatGPT o1, struggles with large-digit multiplication, especially when multiplying numbers beyond nine digits. This is a notable improvement over the previous ChatGPT-4o model, which struggled even with four-digit multiplication, but it’s still a major flaw.

What implications does this have regarding the tool’s ability to reason?

Large-digit multiplication is a useful test of reasoning because it requires a model to apply principles learned during training to new test cases. Humans can do this naturally. For instance, if you teach a high school student how to multiply nine-digit numbers, they can easily extend that understanding to handle ten-digit multiplication, demonstrating a grasp of the underlying principles rather than mere memorization.

In contrast, LLMs often struggle to generalize beyond the data they have been trained on. For example, if an LLM is trained on data involving multiplication of up to nine-digit numbers, it typically cannot generalize to ten-digit multiplication.

As LLMs become more powerful, their impressive performance on challenging benchmarks can create the perception that they can “think” at advanced levels. It’s tempting to rely on them to solve novel problems or even make decisions. However, the fact that even o1 struggles with reliably solving large-digit multiplication problems indicates that LLMs still face challenges when asked to generalize to new tasks or unfamiliar domains.

Why is it important to study how these LLMs “think”?

Companies like OpenAI haven’t fully disclosed the details of how their models are trained or the data they use. Understanding how these AI models operate allows researchers to identify their strengths and limitations, which is essential for improving them. Moreover, knowing these limitations helps us understand which tasks are best suited for LLMs and where human expertise is still crucial.




University of Waterloo




            AIhub is supported by:


Related posts :



Exploring counterfactuals in continuous-action reinforcement learning

  20 Jun 2025
Shuyang Dong writes about her work that will be presented at IJCAI 2025.

What is vibe coding? A computer scientist explains what it means to have AI write computer code − and what risks that can entail

  19 Jun 2025
Until recently, most computer code was written, at least originally, by human beings. But with the advent of GenAI, that has begun to change.

Gearing up for RoboCupJunior: Interview with Ana Patrícia Magalhães

  18 Jun 2025
We hear from the organiser of RoboCupJunior 2025 and find out how the preparations are going for the event.

Interview with Mahammed Kamruzzaman: Understanding and mitigating biases in large language models

  17 Jun 2025
Find out how Mahammed is investigating multiple facets of biases in LLMs.

Google’s SynthID is the latest tool for catching AI-made content. What is AI ‘watermarking’ and does it work?

  16 Jun 2025
Last month, Google announced SynthID Detector, a new tool to detect AI-generated content.

The Good Robot podcast: Symbiosis from bacteria to AI with N. Katherine Hayles

  13 Jun 2025
In this episode, Eleanor and Kerry talk to N. Katherine Hayles about her new book, and discuss how the biological concept of symbiosis can inform the relationships we have with AI.

Preparing for kick-off at RoboCup2025: an interview with General Chair Marco Simões

  12 Jun 2025
We caught up with Marco to find out what exciting events are in store at this year's RoboCup.

Graphic novel explains the environmental impact of AI

  11 Jun 2025
EPFL’s Center for Learning Sciences has released Utop’IA, an educational graphic novel that explores the environmental impact of artificial intelligence.



 

AIhub is supported by:






©2025.05 - Association for the Understanding of Artificial Intelligence


 












©2025.05 - Association for the Understanding of Artificial Intelligence