Kedis ku xidhan hagaha · Dhexdhexaad Heerka

Kediska Tababarka Saadaasha-Token-badan

Dib u eeg khasaaraha calaamadaha badan iyo madax-jir-jireedka la wadaago, oo leh DeepSeek-V3 qaabaynta kala duwan ee isku xigxiga, madax ikhtiraacida ikhtiyaarka ah, iyo kabitaanka bartilmaameedka.

Wadooyinka haga ee la xidhiidhaMulti Token Tababarka Saadaasha
Su'aal 1 ee 8

In the cited Meta study, what architecture predicts several future tokens from a training position?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. In the cited Meta study, what architecture predicts several future tokens from a training position?
    • A mandatory separate complete language model for every word
    • Independent output heads above a shared transformer trunk
    • One head that can target only the preceding token
    • A larger vocabulary with no additional prediction targets
  2. Qaabkee ayaa si gaar ah u isticmaalay ujeeddada saadaalinta calaamado badan ee tababarka ka hor?
    • Word2Vec asal ahaan
    • Kala soocida sawirka convolutional
    • Qaab n-gram ah
    • DeepSeek-V3
  3. What additional supervision does multi-token training supply at a prefix position?
    • Loss terms for multiple future-token targets
    • Only a larger batch size with the same single target
    • A mandatory image-reconstruction target
    • No prediction loss at any position
  4. How are auxiliary predictions used in the Meta paper’s self-speculative inference experiment?
    • They are accepted unconditionally without verification
    • They automatically retrain the model on every prompt
    • They propose tokens that are then checked by the decoding procedure
    • They encrypt every generated token
  5. Maxaa ku dhacaya madaxda kaaliyaha tababarka ka dib haddii aadan u baahneen dardar-gelinta?
    • Waa inay joogaan oo ay labanlaabaan cabbirka moodeelka
    • Waa la tuuri karaa markaa cabbirka geyntu isma beddelin
    • Waxay isu rogaan lakabyo feejignaan ah
    • Waxay noqdaan calaamadeeyaha
  6. Which evaluated task family showed notable gains in the Meta multi-token study?
    • Code generation benchmarks
    • A guarantee of perfect code compilation in every language
    • Only alphabetizing raw filenames
    • A required quantum-sensor prediction task
  7. In a four-head offset design predicting from prefix position t, which token is the fourth head’s target?
    • The token at t−4
    • The token at t+1 for every head
    • Only the token at t itself
    • The token at t+4
  8. What distinguishes DeepSeek-V3’s stated MTP architecture from the cited independent parallel-head implementation?
    • It predicts only tokens before the prefix
    • It requires every target to be an image pixel
    • Sequential auxiliary modules that preserve a causal chain across prediction depths
    • It removes the language-model output vocabulary