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I have this very stupid question and I haven't seen it answered anywhere.

Let's say LLM while training is ingesting a very long book.

The name of the author of the book would appear at the very beginning.

While inference, how does the llm determine that the last chapter of the book is written by so and so author and hence that chunk should be near that author's style.

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The information is probably lost, training on very long inputs is expensive, we usually train on short inputs and only send longer ones at the end. So the author name would not appear together with all the chunks unless it appears on every page in the header, assuming the headers are not stripped out first.

I am wondering the same techniques we use to augment chunks for RAG also the same technique used to augment training data with rich metadata. A preprocessing step which ensures metadata rich text for creating foundational models.

It might not directly learn it, but it could still identify it if it's quoted by others or if there's enough text of the same style with the author's name as context.

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