Logit Bias adanwo

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

1. Kini abosi logit ṣe atunṣe?
   • Dimegilio (logit) ti awọn ami kan pato ṣaaju iṣapẹẹrẹ
   • Awọn iwọn ikẹkọ awoṣe ni pipe
   • Awọn iwọn otutu ti gbogbo tọ
   • Awọn nọmba ti àmi ni awọn ti o tọ window
2. Ni ọpọlọpọ awọn API, kini ojuṣaaju logit ti -100 lori ami-ami kan ṣe?
   • Mu ki àmi 100 igba diẹ seese
   • Ni imunadoko ni idinamọ aami lati ipilẹṣẹ
   • Pa àmi rẹ́ kúrò nínú ọ̀rọ̀ náà títí láé
   • Ilọpo meji iyara awoṣe
3. What numeric range does the documented OpenAI Chat Completions logit_bias accept for a token?
   • From 0 to 1 only
   • From -1 to 1 only
   • From -100 to 100
   • Any string value
4. Iyatọ Logit jẹ bọtini si idanimọ wo?
   • Ọrọ ti o han gbangba
   • Ipo gbolohun
   • Orukọ awoṣe
   • ID aami nomba
5. Nitoripe awọn logiti ti kọja nipasẹ softmax, kini ipa ti fifi irẹwẹsi rere kun?
   • O ṣe agbega iwuwo ojulumo ami naa laipẹ ṣaaju isọdi deede
   • O laini ṣe afikun iṣeeṣe ti o wa titi
   • Ko ni ipa titi ikẹkọ
   • O yọ àmi kuro lati ero
6. Anfani bọtini kan ti irẹjẹ logit lori iṣatunṣe itanran ni pe:
   • Yẹ mu awọn awoṣe ká imo
   • Ko nilo atunṣeto ati pe o kan fun ibeere kan
   • Ṣe alekun iwọn window ti o tọ
   • Ṣiṣẹ nikan lori awọn aworan
7. Why should a developer inspect the tokenizer before applying a bias to a visible word?
   • Token IDs are assigned only after sampling
   • Visible words always equal one token
   • The word may be represented by several token IDs, so biasing one ID may not cover all forms
   • The tokenizer controls the model’s training data
8. What is a practical risk of applying a strong positive bias to one token?
   • It increases the context window
   • It changes the model weights permanently
   • It guarantees the token appears exactly once
   • It can distort generation or produce repetitive, awkward text