ChatGPT: Struggling with Simple Logical Calculations

Introduction

Despite the considerable advancements in artificial intelligence and natural language processing, the ChatGPT model by OpenAI still encounters difficulties when handling relatively simple logical calculations. This article will explore some of the limitations of the model by analyzing a specific user’s study, highlighting where the model went wrong in solving basic logical expressions, and discussing the implications of these shortcomings.

The Problem persists in both version 3.5 and Version 4.

Case Study: Logical Truth Tables

In a recent interaction, a user requested a truth table for multiple logical expressions, but ChatGPT failed to deliver accurate results on multiple occasions. The expressions were as follows:

X: ~A<=>B 
Y: ~(A^B) 
Z: ~B<=>A 
M: (A v B) ^ ~(A^B) 
N: (A v B) ^ ~B<=>A 
O: (A v B) ^ ~A<=>B

After several attempts, ChatGPT struggled to produce an accurate truth table for expression Z, leading to inaccurate results for other expressions.

Interactions

I just was trying to compare these two logical sentences through Truth Table, I tried to provide feedbacks and add more steps to help him/her:

X: (A v B) ^ ~ (A ^ B) 
Y: (A v B) ^ (~A <=>B)

Version 3.5

Tried to provide a reminder to him!

several more tries…

I asked for line by line interpretation. I found out it is comparing A and B, not ~A and B!

More efforts without proper result.

I provided the answer as final effort!

My final effort to get correct result out of it!

Then switched to GPT4, to see if that is better!

Expected 40% better reasoning capability on Ver 4

version 4

And Finally, He provide me this article and I just added this section to it ;-). Much better is writing articles and essays or suggesting how to improve his own article!

User Feedback

Throughout the interaction, the user pointed out errors in the provided truth tables, highlighting the incorrect results for expression Z. Despite receiving feedback, ChatGPT continued to produce inaccurate tables, demonstrating an inability to understand the user’s request and adapt accordingly.

Implications

The inability of ChatGPT to perform simple logical calculations raises concerns about its reliability in more complex problem-solving scenarios. This limitation could lead to misconceptions or misinformation if users rely on the model without verifying its results.

Furthermore, the interaction demonstrates that ChatGPT’s capacity to learn from user feedback is limited. Despite the user’s attempts to guide the model towards the correct answer, the model failed to produce accurate results.

Conclusion

While ChatGPT has made significant advancements in natural language understanding and generation, it is crucial to recognize its limitations, particularly when it comes to logical calculations. Users must be cautious when relying on the model for accurate results and should always verify the information provided.

As AI and natural language processing continue to evolve, developers must focus on improving models like ChatGPT, ensuring that they can handle a wide range of tasks, including simple logical calculations, with higher accuracy and responsiveness to user feedback.

Mahdi Salem,
Richmond Hill, ON,
17-March 2023

P.S: Article main text is by chatGPT itself.