Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

Adalu ti awọn ijinle adanwo

Test MoD’s learned token router, fixed top-k capacity, residual path for skipped tokens, static shapes, and causal routing safeguards.

Jẹmọ itọsọna awọn ọnaAdalu Of Ijinle
Ibeere 1 ti 8

Kini Adalu awọn ijinle yatọ kọja awọn ami?

Awotẹlẹ gbogbo ibeere8 ibeere

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

  1. Kini Adalu awọn ijinle yatọ kọja awọn ami?
    • Elo ni isiro (eyi ti fẹlẹfẹlẹ) kọọkan àmi gba
    • Awọn fokabulari ti a lo
    • Awọn data ikẹkọ
    • Èdè àbájáde
  2. Bawo ni MoD ṣe jẹ asọtẹlẹ isuna iṣiro rẹ?
    • Nipa sisọ idaji awọn iwọn awoṣe silẹ laileto
    • Nipa sisẹ ida oke-k ti o wa titi ti awọn ami ni bulọọki kọọkan
    • Nipa lilo awoṣe ti o yatọ fun gbolohun kọọkan
    • Nipa pipa akiyesi patapata
  3. Kini yoo ṣẹlẹ si awọn ami ti olulana KO yan ni bulọọki ti a fun?
    • Wọn ti paarẹ lati ọkọọkan
    • Wọn ti wa ni rán si lọtọ awoṣe
    • Wọn foju bulọki nipasẹ asopọ ti o ku, ti o kọja nipasẹ ko yipada
    • Wọn ti wa ni iyipada si awọn aworan
  4. Which lab published the Mixture of Depths technique?
    • OpenAI ni ọdun 2019
    • Meta ni ọdun 2015
    • Microsoft ni ọdun 2012
    • Google DeepMind ni ọdun 2024
  5. Kini apapọ MoD pẹlu Adapọ-ti-Amoye gbejade?
    • Ipo, eyiti awọn ipa-ọna lori ijinle mejeeji ati yiyan iwé
    • Awoṣe pẹlu ko si paramita
    • A funfun convolutional nẹtiwọki
    • A tokenizer-nikan eto
  6. Kini idi ti oke-k ti o wa titi ṣe fẹ ju iloro-ami-ami ni MoD?
    • O mu ki awọn awoṣe o wu ID ọrọ
    • O ntọju tensor ni nitobi aimi, eyi ti o jẹ daradara lori hardware
    • O yọ iwulo fun ikẹkọ kuro
    • O mu ki awọn fokabulari iwọn
  7. What does the learned router do at a Mixture-of-Depths block?
    • Selects a new model family for every prompt
    • Predicts labels for the next training batch
    • Scores tokens so the block can select which ones receive full attention and MLP computation
    • Changes the vocabulary size at inference
  8. Why does causal generation in MoD use auxiliary predictors for routing?
    • To let a model read the entire answer before generating
    • To remove the fixed top-k compute budget
    • To convert the sequence into an image
    • To make routing decisions without looking at future tokens