We've trained a model, based on GPT‑4, called CriticGPT to catch errors in ChatGPT's code output. We found that when people get help from CriticGPT to review ChatGPT code they outperform those without help 60% of the time. We are beginning the work to integrate CriticGPT‑like models into our RLHF labeling pipeline, providing our trainers with explicit AI assistance. This is a step towards being able to evaluate outputs from advanced AI systems that can be difficult for people to rate without better tools.

The GPT‑4 series of models, which powers ChatGPT, is aligned to be helpful and interactive through “Reinforcement Learning from Human Feedback” (RLHF⁠). A key part of RLHF is collecting comparisons in which people, called AI trainers, rate different ChatGPT responses against each other.

As we make advances in reasoning and model behavior, ChatGPT becomes more accurate and its mistakes become more subtle. This can make it hard for AI trainers to spot inaccuracies when they do occur, making the comparison task that powers RLHF much harder. This is a fundamental limitation of RLHF, and it may make it increasingly difficult to align models as they gradually become more knowledgeable than any person that could provide feedback.

To help with this challenge, we trained CriticGPT to write critiques that highlight inaccuracies in ChatGPT answers.

CriticGPT’s suggestions are not always correct, but we find that they can help trainers to catch many more problems with model-written answers than they would without AI help. Additionally, when people use CriticGPT, the AI augments their skills, resulting in more comprehensive critiques than when people work alone, and fewer hallucinated bugs than when the model works alone. In our experiments a second random trainer preferred critiques from the Human+CriticGPT team over those from an unassisted person more than 60% of the time.