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OpenAI Enhances ChatGPT: Now Thinks Ten Times More Like a Human!

OpenAI’s New Training Technique: Process Supervision

OpenAI has introduced a new training technique known as “process supervision” to mitigate AI errors or “hallucinations”. These hallucinations refer to instances when AI states something that is not true, such as citing fake legal cases or incorrect historical events. Unlike traditional AI training methods that focus solely on the final answer, this new approach rewards AI for every correct reasoning step. This not only helps AI learn from its mistakes but also encourages more logical thinking and transparency.

Testing Process Supervision

The effectiveness of process supervision was tested on a math problem-solving task. The results showed that the AI trained with process supervision performed better overall. It made fewer mistakes, and its solutions were more akin to a human’s approach. Furthermore, it was less likely to fabricate information.

ChatGPT Math and Reward Model

The process supervision method involves an AI model called ChatGPT Math, which is designed to solve math problems using natural language. It is trained using process supervision with a reward model. The reward model provides feedback and hints for the next logical step after each step taken by ChatGPT Math. This model assigns positive rewards for correct steps, such as adding given equations together.

Transparency and Trustworthiness

This method makes ChatGPT Math more transparent and trustworthy than a model that only gives a final answer without any explanation. It can show its work and explain its reasoning using natural language. OpenAI has also released a large dataset of human feedback to aid further research. This data includes human notes for each step of solving different math problems, which can be used to train new models or check existing ones.

Potential Applications and Improving AI Quality

The potential applications of this training method extend beyond math. It could help AI models write summaries, translations, stories, code, jokes, and more. It could also help AI models answer questions, check facts, or make arguments. The method could improve AI quality and reliability by rewarding each correct step, not just the final result. Ultimately, this could lead to AI systems that can communicate with people in a way that’s easy to understand and trust.

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Picture of Faizan Ali Naqvi
Faizan Ali Naqvi

Research is my hobby and I love to learn new skills. I make sure that every piece of content that you read on this blog is easy to understand and fact checked!

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