The current legal understanding is that according to the current copyright laws the ML models trained on some data are not considered derivative works of that data, so if the data was obtained legally and without other restrictions (e.g. if the researchers sign a contract with the data owner that gives them access to data but agree to certain conditions) then the authors of the data do not have any claim on some rights to the resulting model.
One aspect of this is that historically statistical models calculated from large volumes of text (which is a notion that predates computers, e.g. frequency dictionaries and the whole [sub]field of quantitative corpus linguistics) have been considered facts about that corpus of text and thus not copyrightable at all or (depending on jurisdiction) entitled to different set of protections/limitations assigned to compilations of facts, which give some rights to the people who compiled the facts but no rights to the source of these facts (since facts as such can't be copyrighted).
This also applies to many forms of analysis of audiovisual data, where the copyrights of the source works do not transfer to the results of the statistical or qualitative analysis and can't limit their creation, distribution or sale.
The appropriate analogy to a commercial book or movie is not a translation, but some analysis of it - e.g. a thorough literary review and critique of some book or movie is a separate work with its own copyright, and the original author has no claim on it despite the fact that is (obviously) based on the contents of the work and describes it in great detail. Including verbatim fragments of the work is limited (fair use allows some inclusions but not all), but all the other details are not.
Also, the whole notion of copyrightability of ML model weight files is interesting and IMHO not settled. You could argue that there is some creative expression in forming the model (which would support it being copyrightable) or you could argue that it's a mechanistic result of the application of some algorithm and settings (which have the creative part, and are copyrightable on their own), and so the output can't be copyrightable, no matter how much work (human or machine), time and cost it took - at least in USA copyright law doctrine (e.g. Feist Publications v. Rural Telephone Service) is that mere "sweat of the brow" (no matter how much) does not entitle a work to copyright protection; it requires application of human creativity to create an original work, and automated processes can't satisfy that requirement.
So from the perspective of GPT-3 authors it might be prudent to distribute the model only to people who agree to specific, explicit contractual/licensing restrictions on redistribution and usage of the model instead of simply relying on the default application of copyright laws.
And crucially, if some output is not copyrightable, it can't be considered a derived work to which the authors of a source work could have some claim.