Which method helps address limitations of Foundation Models like bias?

Grounding AI's output to verifiable sources is an effective method for addressing limitations such as bias in Foundation Models. Foundation Models, which are typically trained on vast datasets from the internet, can inadvertently learn and propagate biases present in that data. By grounding the output of these models in verifiable sources, we can encourage more accurate and unbiased responses.

This approach helps ensure that the information generated by the AI is aligned with credible and factual sources, reducing the likelihood of biased or misleading outputs. By referencing established knowledge and proven data, the model's performance can be made more reliable and responsible. Grounding effectively serves to check and balance the model's responses, fostering a higher standard of quality and trustworthiness in the information produced.