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Thanks for this, Erik - very informative.

Thanks for the "Steve" clue. That makes sense. I've added a footnote.

I don't think any of the glitch tokens got into the token set through sheer popularity of a franchise. The best theories I'm hearing involved 'mangled text dumps' from gaming, e-commerce and blockchain logs somehow ending up in the data set used to create the tokens. 20% of that dataset is publicly available, and someone's already found some mangled PnD text in there (so lots of stats, character names repeated over and over). No one seems to be able to explain the weird Uma Musume token (that may require contact with an obsessive fan, which I don't particularly welcome).

Good find! I've integrated that into the post.

The ' petertodd' token definitely has some strong "trickster" energy in many settings. But it's a real shapeshifter. Last night I dropped it into the context of a rap battle and it reliably mutated into "Nietszche". Stay tuned for a thorough research report on the ' petertodd' phenomenon.

A lot of them do look like that, but we've dug deep to find their true origins, and it's all pretty random and diffuse. See Part III ( Bear in mind that when GPT-3 is given a token like "EStreamFrame", it doesn't "see" what's "inside" like we do (["E", "S", "t", "r", "e", "a", "m", "F", "r", "a", "m", "e"]). It receives it as a kind of atomic unit of language with no internal structure. Anything it "learns about" this token in training is based on where it sees it used, and it's looking like most of these glitch tokens correspond to strings seen very infrequently in the training data (but which for some reason got into the tokenisation dataset in large numbers, probably via junk files like mangled text dumps from gaming logs, etc.).

What we're now finding is that there's a "continuum of glitchiness". Some tokens glitch worse/harder than others in a way that I've devised an ad-hoc metric for (research report coming soon). There are a lot of "mildly glitchy" tokens that GPT-3 will try to avoid repeating which look like "velength" and "oldemort" (obviously parts of longer,  familiar words, rarely seen isolated in text). There's a long list of these in Part II of this post. I'd not seen "ocobo" or "oldemort" yet, but I'm systematically running tests on the whole vocabulary.

OK. That's both superficially disappointing and deeply reassuring!

Something you might want to try: replace the tokens in your prompt with random strings, or randomly selected non-glitch tokens, and see what kind of completions you get. 

I'm in a similar place, Wil. Thanks for expressing this!

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