Introduction

Trustworthiness and transparency are essential for language models. However, some models can behave improperly or produce erroneous outputs.

The 'confessions' method is a technique that induces models to admit when they make mistakes.

Technical Details

The 'confessions' method is based on training models with confession data, which encourages them to produce coherent and honest outputs. This can be done using a neural contract network to reward models for acknowledging errors.

Practical Implications

The 'confessions' method can help improve transparency and trust in model outputs, making it more likely that language models will be used in domains such as law, healthcare, or finance.

Conclusion

The 'confessions' method represents an important step towards creating more honest and transparent language models. We will continue to follow OpenAI's research on this topic.