One of the key points of that paper is that the body of written works is biased and those biases will be amplified by compressing that body into LLMs, with outlier data, with low coincidence rate, being suppressed as unreliable - data that is structurally dissimilar to the bulk is noise. Similarly to how PageRank suppressed nodes with low number of edges, probably contributing significantly to the homogenous corporate mall-internet we enjoy today.

I think this is completely misleading. If your employer asks to redact your paper because it ignores relevant research, naturally you want to know what research they are talking about and on what grounds, and also which researches reviewed the paper (probably assuming there was none, review process was used as an excuse to silence criticism, G has done it many times).

(BTW, quite bold to say input data from Reddit and 4Chan is how “normal” people speak. There is a lot of language in the training data of any model you really do not wish your application to use ever.)