T5 ati Idanwo Gbigbe Ọrọ-si-ọrọ

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

1. Kini ero asọye lẹhin T5?
   • O ṣe itumọ ẹrọ nikan
   • O ṣe awọn aworan lati awọn akọle
   • Gbogbo iṣẹ-ṣiṣe NLP ti wa ni ipilẹ bi titẹ ọrọ si iṣelọpọ ọrọ
   • O rọpo gbogbo ikẹkọ pẹlu ẹkọ imuduro
2. Ohun ti Amunawa faaji lo T5?
   • A koodu koodu-decoder ni kikun
   • Encoder-nikan, bi BERT
   • Decoder-nikan, bi GPT
   • Ko si akiyesi rara
3. Ohun pretraining ohun ti T5 akọkọ lilo?
   • Asọtẹlẹ gbolohun ọrọ to nbọ nikan
   • Asọtẹlẹ awọn idiyele ọja
   • Ikun aworan
   • Ibajẹ igba pipẹ, iboju iparada awọn ipari gigun ti o rọpo nipasẹ awọn ami sentinel
4. Koposi wo ni T5 ti kọkọ tẹlẹ?
   • Wikipedia nikan
   • The C4 Colossal Mọ crawled Corpus
   • Atokọ data ti koodu orisun
   • Awọn iwe afọwọkọ ohun
5. Bawo ni T5 ṣe aṣoju iṣẹ-ṣiṣe isọdi bi itupalẹ itara?
   • O ṣejade fekito iṣeeṣe nikan
   • O da ID nomba pada
   • O ṣe ipilẹṣẹ ọrọ aami gangan, gẹgẹbi 'rere' tabi 'odi'
   • Ko le ṣe isọri
6. Kini FLAN-T5 ni ibatan si T5 atilẹba?
   • Awoṣe aworan ti o kere ju
   • Itọnisọna-aifwy-ọmọ ti o ṣe akopọ si awọn iṣẹ-ṣiṣe ti a ko ri
   • Eto idanimọ ọrọ kan
   • Ohun elo data ti ko ni ibatan
7. When T5 generates a summary, what does decoder cross-attention let it consult?
   • The encoder's representations of the input article
   • Only previously generated summary tokens
   • Only the unprocessed task label
   • The training optimizer's stored gradients
8. In a closed-book T5 question-answering setup, where does the answer's factual information come from at inference?
   • A document retrieval step required for every question
   • A newly supplied answer passage required in the prompt
   • A live database lookup built into span corruption
   • Knowledge encoded in the model's learned parameters