Prefix Tuning Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Prefix tuning inodzidzisei chaizvo?
   • Yese yemhando yetransformer uremu
   • Chete chemodhi chinobuda mudura remazwi
   • Ikopi yakasiyana yemuenzaniso webasa rega rega
   • Seti diki yeinoenderera mberi prefix vectors nepo modhi ichiramba yakaomeswa nechando
2. Majekitari echivakashure anobaiwa kupi?
   • Mumakiyi uye kukosha pane yese yekutarisisa layer
   • Chete pane yekupedzisira yekubuda layer
   • Mubasa rekurasikirwa chete
   • Pinda mu tokenizer usati waiswa
3. Ndiani akasuma prefix tuning uye rinhi?
   • OpenAI muna 2018
   • Li naLiang kuStanford muna 2021
   • Google muna 2015
   • Meta muna 2023
4. Nei prefix tuning ichikwezva yekushandira akawanda mabasa?
   • Izvo zvinoda hapana data rekudzidziswa zvachose
   • Inoita kuti inference ishande pane hapana Hardware
   • Basa rega rega rinongoda diki prefix pane yakazara modhi kopi
   • Iyo inonyora zvachose iyo base modhi yemabasa ese kamwechete
5. Ndeapi manomano anowanzoshandiswa kudzikamisa prefix kudzidziswa?
   • Kupeta kaviri mwero wekudzidza nhanho imwe neimwe
   • Kuomesa prefix uye kudzidzisa modhi
   • Kubvisa zvese zvekutarisisa
   • Reparameterizing prefix kuburikidza nediki feed-forward network inozoraswa gare gare
6. Imhando ipi yebasa iyo prefix tuning yainyanya kupenya?
   • Kugadzira mutauro wechisikigo setafura-kune-zvinyorwa uye muchidimbu
   • Kuronga kwemifananidzo
   • Audio waveform synthesis
   • Database indexing
7. What proportion of model parameters did the original prefix-tuning paper report learning in its experiments?
   • About 10%
   • About 0.1%
   • Zvinenge 50%
   • All model parameters
8. How did prefix-tuning perform in the paper’s low-data settings?
   • It always matched full fine-tuning on every task
   • It could not train with limited examples
   • It outperformed full fine-tuning in some evaluated low-data cases
   • It changed every backbone parameter