Why pretrained models should use introspection fact sheets

Pretrained models are powerful tools for natural language processing, but they often lack the ability to answer basic questions about themselves, such as who created them, what is their purpose, and what model architecture they use.

These questions are not only relevant for curious users, but also for researchers and developers who want to understand the strengths and limitations of different models.

One way to address this gap is to use introspection fact sheets, which are short texts that provide essential information about a pretrained model. These fact sheets can be added to the training data as answers to a set of common introspection questions, such as:

• Who created you?
• What is your purpose?
• What model is this?
• What data did you train on?
• How do you handle bias and fairness?
• How do you deal with uncertainty and errors?

By adding these fact sheets to the training data, we can help the pretrained models learn to answer these questions more accurately and consistently. This can improve the transparency and trustworthiness of the models, as well as their usability and interoperability.

However, it is not clear what the best mechanism is for giving foundation models this information aside from a shallow fine-tuning. For example, how should we balance the fact sheet data with the original training data? How should we format and structure the fact sheet texts? How should we evaluate the quality and usefulness of the fact sheet answers? These are open questions that require further research and experimentation.