Idanwo Iṣapeye Ipin Idiwọn

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

1. Kini anfani iwulo akọkọ ti ORPO lori awọn ọna bii DPO?
   • O ṣe imukuro iwulo fun awoṣe itọkasi lọtọ
   • Ko nilo data ikẹkọ rara
   • O ṣiṣẹ nikan lori awọn awoṣe aworan
   • O ṣe onigbọwọ awọn hallucinations odo
2. Kini ipin 'awọn aidọgba' ni ORPO ṣe afiwe?
   • Iyara ikẹkọ dipo deede
   • Awọn aidọgba ti idahun ti o yan dipo esi ti a kọ
   • Meji ti o yatọ eko awọn ošuwọn
   • Iwọn awoṣe dipo iwọn dataset
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. Ipadanu ORPO jẹ apejuwe ti o dara julọ bi apapo wo?
   • SFT agbelebu-entropy pẹlu iwọn awọn aidọgba-ipin ààyò ààyò
   • Nikan ere-awoṣe Dimegilio
   • Ipadabọ ikẹkọ-imuduro mimọ
   • Abẹrẹ ariwo laileto
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?
   • Iwọn fokabulari tokenizer
   • Nọmba awọn olori akiyesi
   • 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