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Perhaps it learns that "humor" is a variety of producing wherever the convention is to convey to a superficially practical tale which then finishes in an (apparently) arbitrary randomly-chosen word… At best, you could pretty generically trace at a subject to test to at the very least get it to use key phrases then you would have to filter by way of quite a couple of samples to get just one that actually wowed you. Use a lot more certain identification when probable, e.g., Cuban, Puerto Rican, Mexican American, but frequently use Latino/Latina somewhat than Hispanic when a broader term is needed. I really don't use logprobs considerably but I normally use them in 1 of three strategies: I use them to see if the prompt ‘looks weird’ to GPT-3 to see where in a completion it ‘goes off the rails’ (suggesting the have to have for lower temperatures/topp or greater BO) and to peek at feasible completions to see how uncertain it is about the suitable respond to-a fantastic example of that is Arram Sabeti’s uncertainty prompts investigation where the logprobs of every probable completion presents you an idea of how properly the uncertainty prompts are doing work in having GPT-3 to place pounds on the suitable response, or in my parity examination where I observed that the logprobs of vs 1 have been almost just 50:50 no matter how quite a few samples I included, displaying no trace whatsoever of couple-shot studying happening

It has probable previously found the finetuning corpus, is aware most of it, and will tractably make poems on demand. Likewise, acrostic poems just do not do the job if we input them generally, but they do if we meticulously expose the appropriate unique letters. This is a minor astonishing to me because for Meena, it produced a substantial big difference to do even a small BO, and although it had diminishing returns, I really do not believe there was any place they analyzed wherever increased very best-of-s designed responses actually considerably worse (as opposed to simply n times additional high priced). And there may possibly be encodings which just function improved than BPEs, like unigrams (comparison) or CANINE or Charformer. Anthropomorphize your prompts. There is no substitute for testing out a number of prompts to see what different completions they elicit and to reverse-engineer what sort of text GPT-3 "thinks" a prompt came from, which could not be what you intend and presume (after all, GPT-3 just sees the handful of text of the prompt-it’s no additional a telepath than you are). There are equivalent challenges in neural equipment translation: analytic languages, which use a comparatively compact range of unique text, aren’t way too poorly harmed by forcing text to be encoded into a set variety of phrases, simply because the get issues additional than what letters every phrase is made of the lack of letters can be created up for by memorization & brute pressure

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