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Nucleus ak Top-k
Top-k and nucleus (top-p) sampling are stochastic decoding methods that truncate a model’s next-token distribution before drawing a token.
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Résumé
Temperature rescales logits and changes distribution sharpness; none of these settings guarantees factual accuracy.
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Holtzman et al. identify a gap between likelihood training and open-ended generation: likelihood-maximizing decoding can produce bland and repetitive text in their experiments. Nucleus sampling retains a dynamic set of tokens containing most probability mass and samples from it, trimming the unreliable tail. Top-k instead fixes the number of candidates. Later analysis explains properties and limitations of truncation heuristics; their findings are scoped to the studied models and tasks. For a no-sampling baseline, greedy decoding selects the highest-scoring next token directly. This removes token-sampling randomness for a fixed model and input; it does not guarantee correct code, and deployment-level reproducibility can also depend on system details.
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Top-k keeps a fixed number of high-probability tokens at a step.
Top-p keeps the smallest probability-ranked set whose cumulative mass reaches the chosen threshold.
Temperature changes the relative sharpness of the distribution before sampling.
When evaluating open-ended outputs, compare decoding settings on diversity, coherence and repetition rather than assuming one setting is best.
Ëlëgu Nucleus ak Top-k
Choose decoding settings by task-specific evaluation and record the model, prompt and sampling parameters for reproducibility. Assess diversity and coherence alongside task success. The cited papers do not establish a universal default or guarantee that sampling prevents repetition or errors.
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What is Nucleus and Top-k Sampling?
Top-k and nucleus (top-p) sampling are stochastic decoding methods that truncate a model’s next-token distribution before drawing a token. Temperature rescales logits and changes distribution sharpness; none of these settings guarantees factual accuracy.
naka la top-k sampling di tënk tànneefi token yi?
Top-k dafay dagg séddale bi ci limu fixe k ci jeton yi gëna am wërsëg, renormalise, ak misaal ci ñoom.
Lan mooy 'nucleus' ci sampling nucleus (ci kaw-p)?
Top-p dafay tëye kuréel gi gëna ndaw ci top tokens yi nga xamni seen probabilite cumulatif yegg na ci p, suko defee ñu jël misaal ci ñoom.
Lan mooy njariñu top-p ci kaw top-k?
Top-p's candidat set dafay wàññeeku sudee model bi wóolu boppam ba noppi màgg sudee wóorul, wuute na ak top-k's count fixe.
Lan la parametru tamperatiir bi di def balaa ñuy jël échantillon?
Tàngoor wu woyof dafay dajale probabilite yi ci token yu kaw yi (gëna deterministik); tàngoor wu gëna yéeg dafay platre (gëna wuute).
What result did the nucleus-sampling paper report for its tested open-ended generation tasks?
Holtzman et al. report that sampling from the dynamic nucleus produced more diverse generations while retaining fluency and coherence in their evaluated setups; this is not a universal guarantee.
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