Korpora VS bookcorpus

- LDA (usually very expensive, but some options exist and in some cases you can google them to find them elsewhere for free): https://www.ldc.upenn.edu/ - Connecting with a university or looking at a linguistics lab's corpus holdings (some will host -- or freely acquired the corpus and therefore you can find it on the internet) - Some language-specific lists or collections: e.g. https://warwick.ac.uk/fac/soc/al/repository/staff/harrisontilly/corpora-for-workshop/, https://github.com/ko-nlp/Korpora , https://guides.uflib.ufl.edu/frenchlinguistics/corpora - Some larger overviews, which may contain links: e.g. https://www.clarin.eu/resource-families/corpora-academic-texts , https://libguides.reed.edu/linguistics/datasets-corpora - Some larger projects to create (often text-based) corpora for multiple languages (often for NLP): e.g. https://www.sketchengine.eu/documentation/tenten-corpora/

The GPT-3 paper (section 2.2) mentions using two datasets referred to as "books1" and "books2", which are 12B and 55B byte pair encoded tokens each.

Project Gutenberg has 3B word tokens I believe, so it seems like it could be one of them, assuming the ratio of word tokens to byte-pair tokens is something like 3:12 to 3:55.

Another likely candidate alongside Gutenberg is libgen, apparently, and looks like there have been successful efforts to create a similar dataset called bookcorpus: https://github.com/soskek/bookcorpus/issues/27). The discussion on that github issue suggests bookcorpus is very similar to "books2", which would make gutenberg "books1"?

This might be why the paper is intentionally vague about the books used?