Exactly the approach that is needed. This approach seems not a lot different from "use your weightings to summarize my new documents". Not that it isn't usefu

Exactly the approach that is needed. This approach seems not a lot different from "use your weightings to summarize my new documents". Not that it isn't useful, but it is not the same as preventing hallucinations. You've merely constrained the workspace to your known set of data, which mostly throws away the entire set of trillions of training data points. Yet, preventing hallucinations straight form the LLM is nontrivial, especially when the LLM is merely generating next-most-likely-words, without any truth value. At the very least, it would require abstracting truthful concepts (abstract objects and relationships) from the data, which is a heck of a lot more than just the clusters of sort-of-synonyms and word clusters that it now has. Then, make weightings between all of those, and generate new series of valid/factual concepts, then put those in words. In contrast to the N parameters (around 100 trillion?), it sound like this would need closer to N!...