quiz bu lëkkale ak tegtal · Dëgër Niveau

Quiz decodage risk bu gëna ndaw ci Bayes

Test yi ñuy seentu ci njariñ, misaali pseudo-royuwaay, méngale kàndidaa quadratic, àppu metric, ak tënk kàndidaa-pool decodage MBR.

Yooni njiit yu jëm ciDekodage Risk Bayes bi gëna ndaw
Laaj 1 de 8

Lan la decodage MBR di tànnee ci barabu benn yoon bu gëna am wërsëg?

Xoolaatal laaj bu nekk8 laaj

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

  1. Lan la decodage MBR di tànnee ci barabu benn yoon bu gëna am wërsëg?
    • Kandidat bi gëna am njariñ buñu seentu ci yeneen génne yuñ mëna am
    • Toppatoo bi gëna gàtt
    • Misaalu aleatër
    • Toppatoo bi gëna gudd
  2. Ginaaw royuwaayi dëgg yi amul, lan la MBR di jëfandikoo ci pseudo-royuwaayi?
    • ab corpus bu nekk ci biti
    • poolu kàndidaa biñ jël muy génne seen bopp
    • Tegtal nit
    • Set de formation
  3. Lu tax MBR naïf seer lool ci wàllu ordinatër?
    • Dafay laaj ñu tàggataat model bi
    • Dafa soxla vocabulaire bu gëna mag
    • Tegtale N kàndidaa ci ñaar mooy O (N kaare) woote metrik
    • Jëfandikuwul benn GPU
  4. What has COMET been used for in minimum Bayes risk machine-translation experiments?
    • As a universal guarantee of better translation quality
    • As a tokenizer for the candidate pool
    • As an exact reference translation available at inference
    • As a learned utility metric in evaluated translation settings
  5. Teg sa bopp ci xalaat mingi aju ci MBR ndax:
    • Sampling chaine yu bari te tann tontu bi gëna déggoo
    • Dafay jël misaalu tontu bi njëkk
    • Jëfandikool seetlu beam ci biir
    • Dafay baña bàyyi xel ci séddaleb model bi
  6. How is risk generally expressed in minimum Bayes risk decision-making?
    • Always one minus a normalized similarity score
    • Expected loss under a chosen loss function and reference distribution
    • The model’s parameter count divided by accuracy
    • The number of candidates sampled
  7. If the chosen utility metric disagrees with human judgments, what will MBR primarily optimize?
    • Human preference automatically overrides the metric
    • The model’s raw next-token likelihood
    • The expected utility under that selected metric
    • The shortest candidate in the pool
  8. If a high-quality answer is missing from the sampled candidate pool, what can MBR do?
    • It guarantees the missing answer is generated
    • It changes the model weights to add the answer
    • It replaces the utility metric with a human reference
    • It can only select or rerank candidates that are in the pool