Igi-ti-ero Ididan adanwo

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

1. Bawo ni Igi-ti-ero ṣe yato si idawọle-ti-ero?
   • O ṣawari awọn ipa ọna ero eka pupọ dipo ẹwọn laini kan
   • O yọ awọn igbesẹ ero kuro patapata
   • O ṣiṣẹ nikan lori awọn igbewọle aworan
   • Ko lo awoṣe ede rara
2. Ninu Igi-ti-ero, kini ipade kanṣoṣo maa n ṣe aṣoju?
   • Idahun ipari pipe nikan
   • Ojutu apa kan tabi agbedemeji 'ero'
   • Ibeere olumulo kan
   • A oṣiṣẹ àdánù
3. Kini ipa wo ni oluyẹwo ipinlẹ ṣe ni Igi-ti-ero?
   • O kọ idahun ti nkọju si olumulo ikẹhin
   • O npa iranti awoṣe
   • O ṣe iṣiro bawo ni awọn solusan apa kan ti o ni ileri jẹ ki awọn ẹka alailagbara le ṣe gige
   • O tumọ laarin awọn ede
4. Lori Ere ti 24 ala, ToT bosipo bori pq-ti-ero ni aijọju nipasẹ kini fo?
   • Lati 90% si 95% aṣeyọri
   • Ko si ilọsiwaju rara
   • Lati 50% si 51% aṣeyọri
   • Lati bii 4% si nipa 74% aṣeyọri
5. Awọn ilana wiwa wo ni a lo nigbagbogbo lati lọ kiri igi ero?
   • Ibu-akọkọ, ijinle-akọkọ, tabi wiwa tan ina
   • Nikan lafaimo lairotẹlẹ
   • Tito lẹsẹsẹ labidi
   • Ilọlẹ-jinle lori awọn piksẹli
6. Kini iṣowo idiyele akọkọ ti lilo Igi-ti-ero?
   • O dinku deede lati fi akoko pamọ
   • O nilo iṣiro inference diẹ sii ni paṣipaarọ fun iṣedede giga
   • Ko nilo awọn ipe awoṣe ju ọkan lọ
   • O ṣiṣẹ offline nikan
7. At one Tree-of-Thoughts node, what does the thought generator typically produce?
   • A final answer only, with no intermediate states
   • A new model checkpoint
   • Several candidate next thoughts that could extend the partial solution
   • A set of training labels for the prompt
8. What decision does the ToT search procedure make after candidate states are generated and scored?
   • Which tokenizer the model should use
   • Whether to change the model’s weights
   • How to label the original training corpus
   • Which branches to expand and which to prune or stop exploring