Quiz zum parallelen Dekodieren des Gedankengerüsts

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1. Was bringt die erste Stufe des Gedankenskeletts hervor?
   • Eine vollständige endgültige Antwort
   • Ein fein abgestimmter Modellkontrollpunkt
   • Ein kurzes Skelett von Punktüberschriften
   • Eine Reihe von Trainingsetiketten
2. Warum kann die Punkterweiterungsstufe parallel ablaufen?
   • Denn jeder Punkt hängt vom vorherigen ab
   • Denn die Punkte sind weitgehend unabhängig voneinander
   • Weil das Modell auf Parallelität umgeschult wird
   • Weil Token rückwärts generiert werden
3. Was sind die größten Engpässe bei SoT-Zielen bei der normalen LLM-Dekodierung?
   • Sequentielle Token-für-Token-Latenz
   • Lesegeschwindigkeit der Festplatte
   • Vokabulargröße des Tokenizers
   • Netzwerkbandbreite für den Benutzer
4. Which answer structure is a good fit for Skeleton-of-Thought's parallel expansion?
   • A proof whose next step must use the previous step's newly derived result
   • A single fixed one-word output with no expansion
   • A procedure that requires each section to wait for all earlier outputs
   • Several sections that can be developed largely independently
5. What extra work should a Skeleton-of-Thought latency comparison include?
   • Only the longest point while ignoring every other stage
   • Generating the outline, coordinating point expansion, and assembling the final response
   • Mandatory full model retraining for every answer
   • A guarantee that overhead is always exactly the same fixed duration
6. How can local batched Skeleton-of-Thought decoding expand multiple points?
   • Generate every token of every paragraph in one complete forward pass
   • Retrain a new model for each heading
   • Advance the point sequences together across repeated decoding steps
   • Remove autoregressive dependencies within every point automatically
7. If Skeleton-of-Thought reduces answer latency, what other assessment remains necessary before adopting the workflow?
   • Treat the speedup as proof that all factual claims are correct
   • Measure only the spelling of the model's name
   • Assume outline ordering guarantees that every section agrees
   • Check whether the assembled response remains coherent and meets answer-quality requirements
8. After concurrent point expansion, how should Skeleton-of-Thought outputs be assembled into the answer?
   • Associate expansions with their skeleton points and combine them in the intended outline order
   • Shuffle the sections randomly regardless of their headings
   • Treat the first finished point as the entire answer
   • Discard the outline and keep only token counts