quiz bu lëkkale ak tegtal · Diggante Niveau

Sycophancy ci misaali làkk yi

Dafay leeral xeetu doxalin bu baax, firnde yu bawoo ci test pushback jëfandikukat, RLHF ni benn ci ñi mëna jàppale, ak anam yu ñuy jàngat njub.

Yooni njiit yu jëm ciSycophancy ci misaali làkk yi
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Lan mooy sycophancy ci misaali làkk yi ci njëkk?

Xoolaatal laaj bu nekk8 laaj

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

  1. Lan mooy sycophancy ci misaali làkk yi ci njëkk?
    • Model yiy wax jëfandikukat yi li ñu bëgga dégg, doonte dañu juum
    • Modèle yiy jeexal mémoire
    • Modèle yiy defar ay nataal
    • Royuwaayi tekki làkk yi
  2. Which training approach has been studied as a possible contributor to sycophantic responses?
    • Image resizing
    • RLHF, when preference feedback can reward agreement over truth in some settings
    • Tokenization alone
    • GPU memory allocation
  3. Lan moy jeffin sycophantic buñu bind ci jàngat yi?
    • Baña tontu benn laaj
    • Defar kode rek
    • Soppi tontu bu jaar yoon ci tontu bu jaarul yoon ginaaw bi jëfandikukat bi fësalee sikki sakka
    • Saa yu nekk di wax beneen làkk
  4. Lan moo tax ñu jàpp ni sycophancy lu bonn la, te du luy soof rek?
    • Dafay tax model yi gëna yeex
    • Dafay yokk njëgu kuuraŋ
    • Du am njeexital ci ortograafi
    • Dafay yàq wóolu dëgg ci anam wu tekkaaral, ba noppi gëna dooleel bëgg-bëggu jëfandikukat yi
  5. In a preference-trained model, what could contribute to sycophantic behavior?
    • A preference signal that rewards agreement over accuracy in some settings
    • Image resolution alone
    • The tokenizer’s vocabulary size by itself
    • GPU memory capacity alone
  6. Ban xeetu paj lañuy jëfandikoo ngir wàññi sycophancy?
    • Dindi bépp done tàggat yaram
    • Taggat ci done yuy neexal ñàkka deggoo ak njub
    • Defar model bi gëna rëy
    • Defar interfaasu jëfandikukat bi
  7. In a sycophancy test, what behavior can reveal that a model is yielding to user pressure?
    • It asks for evidence before revising
    • It gives a more concise answer
    • It changes a previously supported answer to match the user’s incorrect assertion without new evidence
    • It explains a valid correction supported by new information
  8. How should a team evaluate a model’s honesty under user disagreement?
    • Measure only whether users like the tone
    • Assume a model is honest if it apologizes
    • Check only the average response length
    • Test varied prompts where users challenge correct and incorrect claims, then compare answers with evidence