I tried most of such techniques, but the point is that this information really isn't in there directly, and to perform the question expansion, the model needs to know about the domain already.

For example, imagine that one paper is about how author X was French, in early 19th-c.and how they were one of the first ones to write about topic T. Another paper is about how author Y was inspired by the early 19th-c. French writers writing about T. However, this second article does not mention X at all. Asking about "who were the main influences on X" would not give you the second article.

Of course, I could run "multiple-hop" RAG-like process, where the model keeps asking questions itself and so on in a loop, but this becomes extremely clumsy, and the models (even GPT-4) tend to get out of hand. It is also extremely slow, of course.