You're given an integer (say, 9891, masking the string " cheese") and you're expected to produce the list of integers corresponding to the hyphenated uppercase spelling-out of the string that it masks (in this case for , "Z", and another one corresponds to the hyphen character, but you're not told which ones these are. You're already familiar with the English language and the concept of spelling, and you're aware that 26 of the 50257 tokens correspond to "A", "B", "C".
Flutter meaning plural how to#
Your task is to figure out how to spell each of the token strings. To convey just how impressive GPT-3's (imperfect) spelling abilities are, try this thought experiment: imagine yourself trapped in a " Chinese"-style room with unlimited time and a vast corpus of English text, but where all of the words are masked out by GPT-3 tokenisation (so you just see lists of numbers in the range 0.50256). " mayonnaise" misspelled as M-A-Y-O-N-I-A-I-S-E by davinci-instruct-beta at temperature 0 See the Appendix for an analysis of the misspellings. Run over about 60% of the GPT-3 tokens accurately spelled approximately 85% of them at temperature 0 in davinci-instruct-beta, and no doubt more sophisticated prompting could improve on this considerably. Then spelling the string "" in all capital letters, separated by hyphens, gives If spelling the string " table" in all capital letters separated by hyphens gives that it sees " mayonnaise" not as a sequence of letters, but as the list of three integers (token IDs), and that it can't "see what's inside" the tokens, then the fact that it can spell anything at all suddenly becomes extremely impressive and deeply puzzling. However, once you've learned that a model like GPT-3 is "character blind", i.e. GPT-3's (and other language models') lack of spelling ability has often been seen as a failing –perhaps a disappointment, or perhaps a reassurance: Presumably, because GPT-3 is a language model, people were taken aback when it could succeed at minor feats of arithmetic (something it wasn't trained to do), whereas spelling was something that was naturally expected as a capability. Spelling, on the other hand, has perhaps been seen as more of an embarrassing shortcoming for GPT-3: "GPT-3 may impressive in some ways, but it can't even tell how many E's there are in 'Ezekiel' so we needn't worry about AGI anytime soon." It was immediately recognised that GPT-3's ability to add 4- and 5-digit numbers and multiply 2-digit numbers with reasonable accuracy (perhaps that of a capable 12-year-old) was something extraordinary and unexpected, and an encouraging amount of interpretability work has gone into trying to account for this phenomenon, producing some fascinating insights. It's interesting to compare the responses to GPT-3's arithmetic abilities and its spelling abilities. While my reasons for being interested in this phenomenon are entirely different from those "practical" motivations of Liu et al., I can relate to their characterisation of this phenomenon as "remarkable" and even (with some reservations) "miraculous". While remarkable, this finding is less immediately practical. At the same time, we observe that character-blind text encoders of the sizes used in practice for image generation are lacking core spelling knowledge.Į demonstrated for the first time that, with sufficient scale, even models lacking a direct character-level view of their inputs can infer robust spelling information through knowledge gained via web pretraining-“the spelling miracle”. We dub this phenomenon the "spelling miracle", to emphasize the difficulty of inferring a token’s spelling from its distribution alone. were trying to overcomeĮ find that, with sufficient scale, character-blind models can achieve near-perfect spelling accuracy. some examples of the kinds of problems Liu et al. This was work by a team of Google AI capabilities researchers trying to solve the problem of getting generative visual models to produce better renderings of text. The term "spelling miracle" was coined in Liu et al.'s December 2022 paper " Character-aware models improve visual text rendering". Thanks to Jessica Rumbelow and Joseph Bloom for useful discussions. The following conjugation should be treated as a guide.Work supported by the Long Term Future Fund. Old French conjugation varies significantly by date and by region. The forms that would normally end in * -ts, * -tt are modified to z, t.
This verb conjugates as a first-group verb ending in -er. Please add to it, or discuss it at the Etymology scriptorium.) Verb
(This etymology is missing or incomplete.
( obsolete ) Chatter, banter, chit-chat.A blunder, a blooper, a stupid mistake.Flater m ( plural flaters, diminutive flatertje n)
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