Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

Interpolation ipo fun Idanwo Ọrọ Gigun

Checks how positional interpolation rescales RoPE positions, why extrapolation can destabilize attention and what its source evaluated.

Jẹmọ itọsọna awọn ọnaInterpolation ipo Fun Ọrọ Gigun
Ibeere 1 ti 8

Kini ero pataki lẹhin Interpolation Ipo?

Awotẹlẹ gbogbo ibeere8 ibeere

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

  1. Kini ero pataki lẹhin Interpolation Ipo?
    • Lilọ awọn atọka ipo tuntun sinu ibiti awoṣe ti ni ikẹkọ tẹlẹ lori
    • Ikẹkọ awoṣe tuntun-tuntun lati ibere fun gigun kọọkan
    • Yiyọ alaye ipo kuro patapata
    • Tito awọn ami nipasẹ pataki
  2. Kini idi ti isọdi aimọkan si awọn ipo to gun kuna?
    • O nlo iranti kekere ju
    • O le fẹ awọn ikun akiyesi si titobi pupọ, awọn iye riru
    • O nigbagbogbo da aami kanna pada
    • O nilo tokenizer ti o yatọ
  3. Ni aijọju bawo ni iṣatunṣe itanran ti iṣẹ PI atilẹba nilo lati fa LLAMA si 32K?
    • Ko si itanran-yiyi ni gbogbo
    • Nipa awọn igbesẹ 1 million
    • Nipa awọn igbesẹ 1,000
    • A ni kikun pretraining run
  4. Ti o ba faagun ọrọ-ọrọ nipasẹ ifosiwewe s, bawo ni PI ṣe yipada ipo m?
    • O maapu m si m igba s
    • O maapu m to m squared
    • O fi oju m ko yipada
    • O maapu m to m pin nipasẹ s
  5. Bawo ni PI ṣe ni ipa awọn ọna nigbamii bi YaRN?
    • O di ipilẹ ti awọn ọna imọ-igbohunsafẹfẹ ni ilọsiwaju lori
    • Ti o ti abandoned ati ki o ko toka
    • O rọpo Amunawa faaji
    • O kan si awọn awoṣe aworan nikan
  6. Iyipada ipo wo ni Interpolation Ipo ipo ni akọkọ fojusi?
    • Ọkan-gbona awọn koodu
    • RoPE (Awọn ifibọ ipo Rotari)
    • Word2Vec fekito
    • Apo-ti-ọrọ ka
  7. A model trained to position 2K tokens is extended to 32K using PI. What is the extension factor s?
    • 2
    • 8
    • 16
    • 32
  8. With extension factor s = 16, where does PI map original position m = 16,000?
    • Position 16,000
    • Position 256,000
    • Position 32,000
    • Position 1,000