Google's new tool lets large language models fact-check their responses

The first of the two methods is called Retrieval-Interleaved Generation (RIG), which acts as a sort of fact-checker. If a user prompts the model with a question--like "Has the use of renewable energy sources increased in the world?"--the model will come up with a "first draft" answer. Then RIG identifies what portions of the draft answer could be checked against Google's Data Commons, a massive repository of data and statistics from reliable sources like the United Nations or the Centers for Disease Control and Prevention. Next, it runs those checks and replaces any incorrect original guesses with correct facts. It also cites its sources to the user.