Quiz buy gëna xéewale tànneefi Rasio Odds

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

1. Lan mooy njariñu ORPO ci anam yu melni DPO?
   • Dafay dindi soxla wu wuute ci xeetu royuwaay
   • Du soxla benn done tàggat yaram
   • Ci modelu nataal rek lay dox
   • Dafay garanti ni du am benn gis-gis bu jaarul yoon
2. Lan mooy 'odds ratio' bi ci ORPO?
   • Gaawaayu tàggat yaram ak njub
   • Lu mëna am ci tontu biñ tànn ak tontu biñ bañ
   • Ñaari tolluwaayu jàng yu wuute
   • Dayo model bi ak dayo done yi
3. What form does an ORPO preference example contain?
   • Only a single unlabeled response
   • A prompt with two identical completions
   • A prompt with one chosen response and one disfavored response
   • A reward score with no associated responses
4. How does ORPO convert a response probability p into odds?
   • One minus p divided by p
   • The square root of p
   • The logarithm of the prompt length
   • p divided by one minus p
5. Ban boole lañu gëna mëna wax ni ORPO ñàkk?
   • Entropi croisé SFT boole ci ab terme taamu ratio odd pondéré
   • Benn poñ buy neexal kese
   • Dellu jàng-jàngat bu sell
   • Pikir bruit aleatoire
6. Which response receives the supervised fine-tuning component of ORPO?
   • Only the rejected response
   • The chosen or preferred response
   • A response sampled after deployment
   • No response; ORPO has no language-modeling term
7. Which parameter controls the weight of ORPO’s preference term relative to the supervised loss?
   • Dayo vokabileer tokenizer bi
   • Limu nit ñiy bàyyi seen xel ci
   • The preference-loss coefficient
   • The prompt’s character count
8. Why should an ORPO result report its preference data and evaluation setup?
   • ORPO results are identical across datasets
   • The objective removes the need for evaluation
   • The number of model layers determines every preference
   • Outcomes depend on the dataset, coefficient, base model, and evaluation