Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

Idanwo Iyipada Ti ara ẹni

Explains majority-voting over sampled reasoning paths, answer extraction, inference cost, and why consensus can still be wrong.

Jẹmọ itọsọna awọn ọnaIyipada Iduroṣinṣin ti ara ẹni
Ibeere 1 ti 8

Báwo ni yíyan-ìdíwọ̀n ara-ẹni ṣe ń mú ìdáhùn ìkẹyìn láti inú àwòṣe èdè kan?

Awotẹlẹ gbogbo ibeere8 ibeere

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

  1. Báwo ni yíyan-ìdíwọ̀n ara-ẹni ṣe ń mú ìdáhùn ìkẹyìn láti inú àwòṣe èdè kan?
    • Ayẹwo ọpọlọpọ awọn ọna ero ati ki o gba ibo to poju lori idahun
    • Nigbagbogbo mu aami kan ti o ṣeeṣe julọ ni igbesẹ kọọkan
    • Kọ awoṣe lori awọn abajade tirẹ leralera
    • Yọ gbogbo randomness lati iran
  2. Under what condition can self-consistency improve answer accuracy?
    • Whenever the number of samples increases
    • When correct sampled paths converge and errors are not too strongly correlated
    • When every reasoning chain has identical wording
    • Only when the model weights are retrained
  3. Ọna iyipada wo ni iduroṣinṣin ara ẹni rọpo?
    • Itankalẹ afẹyinti
    • Àmi
    • Ipinnu ojukokoro ti ẹwọn ẹyọkan
    • Pipin iwuwo
  4. Eyi ti apa ti kọọkan apere pq ti wa ni kosi lo ninu ik Idibo?
    • Ọrọ ero ni kikun
    • Awọn nọmba ti àmi ti ipilẹṣẹ
    • Eto iwọn otutu
    • Nikan ni ik idahun
  5. What is the main cost of increasing the number of self-consistency samples?
    • More inference compute; accuracy gains depend on the task and can have diminishing returns
    • It retrains the model weights after every response
    • It guarantees that the majority is correct
    • It eliminates the need to sample any reasoning paths
  6. Iduroṣinṣin ti ara ẹni jẹ apẹẹrẹ kutukutu ti imọran gbooro wo?
    • Awoṣe funmorawon
    • Wiwọn akoko ifọkansi (lilo iṣiro afikun lati ronu dara julọ)
    • Augmentation data nigba ikẹkọ
    • Hardware isare
  7. Why may answer normalization be useful before a self-consistency vote?
    • It makes all reasoning paths independent
    • It verifies the answer against external evidence
    • It can group semantically equivalent answers that use different formatting
    • It changes the model’s sampled probabilities
  8. What is a key limitation of a majority answer from self-consistency?
    • A majority vote cannot be computed for text
    • Sampling always changes the model weights
    • Every reasoning path is guaranteed independent
    • Correlated reasoning errors can make an incorrect answer win the vote