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Anthropomorphize your prompts. There is no substitute for tests out a range of prompts to see what diverse completions they elicit and to reverse-engineer what type of textual content GPT-3 "thinks" a prompt arrived from, which may perhaps not be what you intend and presume (after all, GPT-3 just sees the handful of text of the prompt-it’s no far more a telepath than you are). And there may perhaps be encodings which just perform far better than BPEs, like unigrams (comparison) or CANINE or Charformer. OA’s GPT-f work on employing GPT for MetaMath official theorem-proving notes that they use the conventional GPT-2 BPE but "preliminary experimental results demonstrate achievable gains with specialized tokenization tactics." I marvel what other refined GPT artifacts BPEs may well be triggering? This is indeed very a obtain, but it is a double-edged sword: it is baffling to create code for it mainly because the BPE encoding of a text is unfamiliar & unpredictable (adding a letter can modify the final BPEs fully), and the consequences of obscuring the genuine figures from GPT are unclear

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DutytoDevelop on the OA community forums observes that rephrasing quantities in math difficulties as prepared-out words like "two-hundred and one" seems to strengthen algebra/arithmetic functionality, and Matt Brockman has noticed additional rigorously by screening 1000's of illustrations more than many orders of magnitude, that GPT-3’s arithmetic means-incredibly very poor, given we know considerably smaller sized Transformers operate perfectly in math domains (eg. I verified this with my Turing dialogue example in which GPT-3 fails poorly on the arithmetic sans commas & very low temperature, but usually will get it accurately correct with commas.16 (Why? More penned text might use commas when writing out implicit or express arithmetic, certainly, but use of commas might also greatly lessen the amount of exceptional BPEs as only 1-3 digit quantities will seem, with dependable BPE encoding, as a substitute of possessing encodings which vary unpredictably in excess of a a lot larger sized variety.) I also be aware that GPT-3 increases on anagrams if provided area-separated letters, inspite of the point that this encoding is 3× much larger. Nostalgebraist reviewed the excessive weirdness of BPEs and how they alter chaotically based on whitespace, capitalization, and context for GPT-2, with a followup article for GPT-3 on the even weirder encoding of figures sans commas.15 I go through Nostalgebraist’s at the time, but I didn’t know if that was genuinely an challenge for GPT-2, simply because problems like absence of rhyming might just be GPT-2 staying stupid, as it was instead silly in lots of strategies, and illustrations like the spaceless GPT-2-tunes design were ambiguous I held it in brain while assessing GPT-3, nonetheless

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