Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

Igi ti ero adanwo

Tests thought generation, state evaluation, search and backtracking, plus the task-specific limits of reported benchmark results.

Jẹmọ itọsọna awọn ọnaIgi Ero
Ibeere 1 ti 8

Bawo ni Igi Awọn ero ṣe yato ni ipilẹṣẹ si itọsi-pipin-ero?

Awotẹlẹ gbogbo ibeere8 ibeere

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

  1. Bawo ni Igi Awọn ero ṣe yato ni ipilẹṣẹ si itọsi-pipin-ero?
    • O ṣawari awọn ẹka ero pupọ ati pe o le ṣe afẹyinti
    • O ṣe atunṣe awoṣe lori data tuntun
    • O yọ gbogbo awọn igbesẹ ero agbedemeji kuro
    • O ṣiṣẹ nikan fun iran aworan
  2. Awọn ẹya mẹta wo ni o jẹ eto Igi ti Awọn ero?
    • Tokenizer, ifibọ, ati decoder
    • Olupilẹṣẹ ero, oluyẹwo ipinlẹ, ati algorithm wiwa
    • Encoder, ori akiyesi, ati softmax
    • Iṣeto data, iṣapeye, ati iṣẹ isonu
  3. In the original Tree of Thoughts Game of 24 experiment, what result did breadth-five search report for its selected test set?
    • 100% success on every possible puzzle
    • 74% for every model and search budget
    • 74% success for GPT-4 with ToT, versus 4% for a single chain-of-thought baseline
    • 4% for ToT and 74% for chain-of-thought
  4. Bawo ni eto Igi Awọn ero ṣe n ṣe iṣiro awọn ojutu apa kan?
    • Nipa bibeere aaye data ita kan
    • Nipa wiwọn GPU otutu
    • Nipa kika awọn nọmba ti àmi lo
    • Nipa titari awoṣe lati ṣe oṣuwọn awọn ipinlẹ bi idaniloju, boya, tabi ko ṣeeṣe
  5. Kini otitọ nipa imuse Igi Awọn ero lori awoṣe to wa tẹlẹ?
    • O jẹ itọsi ati ipari wiwa, kii ṣe atunṣeto
    • O nilo atunṣe awoṣe lati ibere
    • O le nikan ṣiṣẹ lori specialized hardware
    • O ṣe iyipada awọn iwuwo awoṣe patapata
  6. Ilana ti o jọmọ wo ni o jẹ ki awọn ọna ero dapọ ati tun lo awọn abajade agbedemeji?
    • Ipadasẹyin laini
    • Awonya ti ero
    • Ipese deede
    • Piredi iwọn tan ina
  7. Which components are commonly specified in a Tree of Thoughts setup?
    • A new tokenizer, an optimizer, and a training corpus
    • A database, a browser, and a GPU driver
    • Thought proposals, an intermediate-state evaluator, and a search procedure
    • A label encoder, a loss scaler, and a checkpoint
  8. Why can Tree of Thoughts increase inference cost?
    • It always retrains the base model
    • It requires a separate model for every token
    • It eliminates all model calls after the first thought
    • It may generate and evaluate multiple candidate states across search steps