ALiBi Ipo Bias adanwo

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

1. Kini ALiBi duro fun?
   • Ifarabalẹ pẹlu Awọn Iwa Laini Laini
   • Alabamubadọgba Onirẹjẹ Onigbese
   • Iyipada Iyipada Bidirectional
   • Atọka Laini pipe
2. Bawo ni ALiBi ṣe jẹ ijiya awọn ami ti o jinna?
   • Nipa yiyọkuro awọn ifibọ kọja window ti o wa titi
   • Nipa fifi ijiya kan kun aaye si aaye laarin ibeere ati awọn ipo bọtini
   • Nipa kikọ ẹkọ ifibọ lọtọ fun gbogbo ipo
   • Nipa yiyi ibeere naa ati awọn ipadabọ bọtini
3. Kini anfani ilowo ti ALiBi ṣe ayẹyẹ julọ?
   • O ṣe imukuro iwulo fun softmax
   • O ṣe akiyesi ṣiṣe ni iranti igbagbogbo
   • O ṣe afikun daradara si awọn ilana to gun ju awọn ti a rii ni ikẹkọ
   • O yọ iwulo fun awọn ori akiyesi pupọ
4. Kini iyatọ nipa aiṣedeede laini kọja awọn ori akiyesi?
   • Ori kọọkan pin ipin kanna gangan ite
   • Awọn oke ni a kọ lakoko ikẹkọ
   • Ori akọkọ nikan ni o gba abosi
   • Ori kọọkan gba ite ti o wa titi tirẹ, ti o n ṣe ilana jiometirika kan
5. Ti a ṣe afiwe si awọn ifibọ ipo ibilẹ, ALiBi ṣafikun melo ni awọn aye ti kọ ẹkọ?
   • Ko si - awọn oke aiṣedeede ti wa titi, ko kọ ẹkọ
   • Ọkan fun ipo
   • Ọkan fun àmi
   • Meji fun ori akiyesi
6. Awoṣe nla ti a mọ daradara wo lo ALiBi fun alaye ipo?
   • GPT-2
   • BLOOM
   • BERT
   • Ọrọ2Vec
7. At what stage does ALiBi apply its distance-based bias?
   • To token IDs before they enter the embedding table
   • To the final vocabulary probabilities after decoding
   • To query-key attention scores before the softmax
   • To the training labels after loss calculation
8. Why can ALiBi support inference on sequences longer than those used in training?
   • It removes the need for attention at longer lengths
   • It automatically retrains the model while generating
   • It stores a complete answer for every possible sequence length
   • Its distance-based bias does not require a separately learned embedding for every absolute position