A common lament of new fiction authors is the horribly named "slush pile," where submissions sit waiting for a lit agency "intern" to look at the length of the first few paragraphs and bin the ms. of the next Zadie Smith or Thomas Pynchon
So I wonder, could an LLM do a lot better?
Functionally slush piles are just ignored and have been for awhile. Literary agencies and publishers look for writers with a social media presence now, as it has a major impact on whether the book can be marketed successfully.
I don’t think a model will be able to handle the abstract concepts that make a good book worth reading.. it would be selecting for something predictable/boilerplate…
That said there’s zero doubt in my mind that they will be used heavily in the publishing industry.
I don't know if an LLM could make such a judgement "a lot better" in comparison to an intern, but maybe it can be used in a more useful manner? For e.g. maybe it is valuable for an LLM to tell the agency "67% probability of being similar to the mid-performing books of the last 2 years".
I don't know anything about this world, but judging a book of the length of the first paragraph seems incredibly shallow and easily gamed.
AI might be good at flagging books that deserve a second look. It can match similarly of a manuscript to commercially successful books.
Indirectly, of course, as all new fiction is written by AI, the "slush pile" problem will go away.
Maybe can randomly sample it instead. Would be similar to opening a book to a random page and reading it; not sure how well that would work though.