[stevenhuang]

I found the "grounding" explanation provided by human feedback very insightful:

> Why is this significant? At the core the model is still doing language modeling, right? learning to predict the next word, based on text alone? Sure, but here the human annotators inject some level of grounding to the text. Some symbols ("summarize", "translate", "formal") are used in a consistent way together with the concept/task they denote. And they always appear in the beginning of the text. This make these symbols (or the "instructions") in some loose sense external to the rest of the data, making the act of producing a summary grounded to the human concept of "summary". Or in other words, this helps the model learn the communicative intent of the a user who asks for a "summary" in its "instruction". An objection here would be that such cases likely naturally occur already in large text collections, and the model already learned from them, so what is new here? I argue that it might be much easier to learn from direct instructions like these than it is to learn from non-instruction data (think of a direct statement like "this is a dog" vs needing to infer from over-hearing people talk about dogs). And that by shifting the distribution of the training data towards these annotated cases, substantially alter how the model acts, and the amount of "grounding" it has. And that maybe with explicit instructions data, we can use much less training text compared to what was needed without them. (I promised you hand waving didn't I?)

[seydor]

the only word grounded there was the word "summary" . There are so many more which are not possible to be delivered in the same way