I partially agree with you, but I think you're missing the end goal of Facebook et al.
As HughJanus pointed out it's not really any different than a person reading a book and by that reasoning using copyrighted material to train models like these falls well within the existing framework of "fair use".
However, that depends entirely on "the purpose and character of the use, including whether such use is of a commercial nature or is for nonprofit educational purposes." I agree completely with you that OpenAI's products/business (the most blatant violator) does easily violate "fair use" due to that clause. However they're doing it, at least partially, to "force the issue" on the open question of "how much can public information be privatized?" with the goal of further privatizing and increasing commercial applications of raw data.
As you pointed out LLMs can only create facsimiles and not the original work, and by that logic they can't exactly replicate the inputs either.
No I don't think artists can claim that they own any and all "cheap facsimiles" of their works, but by that same reasoning none of these models produced should be allowed to be the enforceable property of any individual/company either.
For further reading check out:
- Kelly v. Arriba Soft Corporation on why "thumbnails" (and by extension LLMs, "eigen-images", etc.) are inherently transformatve and constitute fair use.
- Bridgeport Music, Inc. v. Dimension Films for the negative impacts that ruling has had and how it still doesn't protect the artists from their stuff being used for training and LLM.
- "Variational auto-encoders" for understanding on how the latest LLMs actually do achieve a significant amount of "originality" and I would argue are able to be minimally creative.
