Maswali ya Upendeleo wa Logit

Majibu na maelezo huonekana unapojibu maswali.

1. Je, upendeleo wa logi hurekebisha nini?
   • Alama (logi) ya tokeni maalum kabla ya sampuli
   • Uzito wa mafunzo ya mfano wa kudumu
   • Joto la haraka nzima
   • Idadi ya ishara kwenye dirisha la muktadha
2. Katika API nyingi, upendeleo wa logi ya -100 kwenye ishara hutimiza nini?
   • Hufanya ishara kuwa mara 100 zaidi
   • Inakataza kwa ufanisi ishara kutoka kwa kuzalishwa
   • Inafuta ishara kutoka kwa msamiati milele
   • Huongeza kasi ya mfano maradufu
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. Upendeleo wa logi umewekwa kwa kitambulisho gani?
   • Neno la maandishi wazi
   • Msimamo wa sentensi
   • Jina la mfano
   • Kitambulisho cha tokeni ya nambari
5. Kwa sababu logi hupitishwa kupitia softmax, ni nini athari ya kuongeza upendeleo mzuri?
   • Inaongeza uzani wa jamaa wa ishara kabla ya kuhalalisha
   • Inaongeza uwezekano usiobadilika
   • Haina athari hadi mafunzo
   • Inaondoa ishara kutoka kwa kuzingatia
6. Faida kuu ya upendeleo wa kumbukumbu juu ya urekebishaji mzuri ni kwamba:
   • Inaboresha ujuzi wa mfano
   • Haihitaji mafunzo tena na inatumika kwa kila ombi
   • Huongeza ukubwa wa dirisha la muktadha
   • Inafanya kazi kwenye picha pekee
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