Getting Accurate Math Answers With ChatGPT (GPT-4)
Getting Accurate Math Answers With ChatGPT (GPT-4)
Using GPT-4's Code Interpreter and Code-base Self-Verification (CSV)
In recent times, the world of Large Language Models (LLMs) has seen exponential growth, notably with models like GPT-4 and PaLM-2. These models have revolutionized how we approach math reasoning problems, and with OpenAI's latest innovations, they're only getting better.
GPT-4's Recent New Avatar: The Code Interpreter
OpenAI fine-tuned GPT-4 to run Python code, it’s called the GPT-4 Code Interpreter, this model has showcased exemplary performance on some of the most challenging math datasets available. This Code Interpreter, dubbed GPT4-Code, has the prowess to:
Offer logical natural language reasoning.
Generate and execute Python code step by step.
Deliver the executed code's results back to the LLM, enhancing its decision-making.
The Magic Ingredient: Code-based Self-Verification (CSV)
GPT-4's success isn't magic, but it sure feels like it. The key lies in its unmatched capability to generate and execute code, evaluate the outcomes, and make necessary corrections based on unreasonable outputs. This entire mechanism is backed by the GPT-4 Code Interpreter.
To further enhance this process, Code-based-Self-Verification (CSV) can be used. This method amplifies GPT-4's mathematical reasoning potential. In cases where the self-verification process identifies discrepancies, GPT-4 takes the lead, amending its solutions, and rectifying errors. This is not just about generating and executing code; it's about the model's ability to adjust its strategies based on feedback.
CSV guides GPT4-Code to:
Generate additional verification code.
Refine reasoning steps in case of discrepancies.
The outcomes?
Incorrect solutions are promptly rectified (corrected).
Solutions that are verified resemble the reliability of human problem-solving.
From GPT-4 to GPT4-Code: A Comparative Analysis
When GPT4-Code was put to the test, it recorded an impressive 69.7% accuracy on the intricate MATH dataset. A significant leap from GPT-4's previous score of 42.2%. But with the Code-based Self-Verification approach, GPT4-Code further pushed its boundaries, achieving an astounding accuracy of 84.32%.
Prompt Examples: GCD and LCM
Basic prompt: "Solve the problem and put your answer in \bracket{}. The problem is: The greatest common divisor of positive integers m and n is 6. The least common multiple of m and n is 16. What is the least possible value of m + n?"
Code-based Self-Evaluation prompt: "Solve the problem using the code interpreter step by step, and please verify your answer using the code interpreter. This problem is: The greatest common divisor of positive integers m and n is 6. The least common multiple of m and n is 16. What is the least possible value of m + n?"
Experience it Yourself: Trying Out GPT4-Code
For the enthusiasts, here's how you can dive in:
Acquire a ChatGPT Plus subscription.
Navigate to 'settings' -> 'beta features' and activate the Code Interpreter.
Choose the GPT-4 tab and opt for the Code Interpreter.
Insert the Code-based Self-Evaluation prompt with your problem and watch the magic unfold.
Conclusion
GPT-4 and its Code Interpreter are changing the game in mathematical problem-solving. As technology progresses, we're on the brink of reshaping the landscape of automated math challenges, all thanks to models like GPT-4.
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