{"about":"Lesson L15, decoding. Computed by tools/school/fill_decoding.mjs from system/school/decoding.mjs, corpus-grammar.mjs and the attention lab's trained model. Do not edit by hand. Entropies are in nats.","modelSha256":"67600098679443bf87c78f0f3ef1a1399ae51642b3e9608517105d7831219d5e","modelFile":"media/school/attention/model.json","method":{"about":"Each prompt starts at an episode boundary. The model continues it until its 24-word context is full (no sliding window). Every sampled setting draws 1500 continuations; row b uses its own random stream (mulberry32, seed 1 + b), the same streams for every setting, so settings differ by their rule, not their luck. Greedy is deterministic and drawn once. Temperature divides the logits before the softmax; top-k keeps the k most likely words; top-p keeps the smallest set of most likely words whose probability reaches p; both renormalise and use temperature 1. 'generator' is the reference: 1500 continuations written by the corpus generator itself (exact conditional sampling, seeds 500000 + b).","grammatical":"Share of continuations that the corpus generator could have written after the prompt, checked by parsing prompt plus continuation with the generator's rules, including the look-back rules (the cone goes on the found thing; the answer is the guide who did it). A continuation cut off mid-sentence counts if it can still be finished.","bigramValid":"Share of continuations whose every adjacent word pair (from the prompt's last word on) occurs in the model's training text. bigramValidButUngrammatical: passes the pair check but fails the grammar, which is the look-back errors a pair check cannot see.","diversity":"distinct-n: distinct n-grams over all n-grams, pooled over the continuations of one setting (Li et al. 2016 style); with a 46-word grammar and many continuations the ratio is small for every setting, so distinct2Count and distinct3Count give the raw number of different word pairs and triples used. uniqueShare: distinct whole continuations over n; distinctTexts: their count. unseenPair: share of continuations with at least one adjacent word pair never seen in training (1 minus bigramValid).","loops":"loop3: the continuation ends with a phrase of two or more words repeated three times in a row (the repetition rule of Holtzman et al. 2020, Figure 9). loop2: the same with two repeats; the generator row gives the rate in text written by the rules, where some repeats are legitimate. repeatedSentence: some complete sentence of three or more words appears twice in prompt plus continuation. Greedy is one continuation per prompt, so its loop rates are 0 or 1, not rates.","entropy":"entropy: mean entropy of the distribution the setting samples from, over every step of every continuation. modelEntropy: the trained model's own (temperature 1) distribution at the same steps. modelPerplexity: exp of minus the model's mean log probability of the chosen words. The generator row's entropy is the generator's own uncertainty after the same prompt.","limits":"Shares are over 1500 continuations from one model, three prompts and one stream of seeds; a share near 0.5 has a binomial standard error near 0.013. This model is small and its corpus is a grammar of 46 words, so a setting that wins here is not shown to win on open-ended text; the papers' claims are about large models and human text."},"papers":{"fan2018":"Fan, Lewis and Dauphin (2018), Hierarchical Neural Story Generation, arXiv 1805.04833: generate stories by sampling from the k = 10 most likely words; they report beam search tends to produce common phrases and repetitive text.","holtzman2020":"Holtzman, Buys, Du, Forbes and Choi (2020), The Curious Case of Neural Text Degeneration, ICLR 2020, arXiv 1904.09751: nucleus sampling samples from the smallest set of words whose total probability reaches p; maximization-based decoding gets stuck in repetitive loops, while pure sampling from the full distribution drifts into incoherence through the low-probability tail."},"shapes":{"about":"The trained model's next-word distribution after 'tallow found a' (log of the temperature-1 probabilities used as scores, which leaves every 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