FAIR publishing some research into long form text generation is basically unrelated to someone generating wikipedia articles and automatically uploading them. Researchers love using wikipedia in various ways because it's free and pretty high quality, and there's a lot of it. So tons and tons of publications do various things to and with wikipedia data. 

Yes maybe someone can download their code and do something nefarious, but I doubt it's any more useful for that sort of thing than other long form text generation approaches like GPT3.

I'm positive that as these language models become more accessible and powerful, their misuse will grow massively. However, I believe open sourcing is the best option here; having access to such model allows us to create accurate automatic classifiers that detect outputs from such models. Media websites (e.g. Wikipedia, Twitter) could include this classifier in their pipeline for submitting new media.

Making such technologies closed source leaves researchers in the dark; due to the scaling-transformer hype, only a tiny fraction of the world's population have the financial means to train a SOTA transformer model.

Presumably it'd take less manpower to review each article that the AI's written (i.e. read the citations & make sure the article accurately describes the subjects) than it would to write articles from scratch. I'd guess this is the case even if the claims seem plausible & fact-checking requires a somewhat detailed reading through of the sources.

I think on balance this is a good thing.  More, better, fairer information on wikipedia is awesome.  
I do worry about different standards of editing for machine- and human-generated contributions, and I foresee some pain in edit wars and reversions until processes and norms evolve to handle mixed-source topics.