Kediska Soo Celinta Isdhexgalka ee ColBERT

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Sidee ColBERT u matalaysaa weydiin ama dukumeenti?
   • Sida halbeegga isku-dhafka celceliska ah
   • Sida erayga muhiimka ah ee cayriin loo tiriyo kaliya
   • Sida badan oo ah calaamado-heer-kooxeed hufnaan ku dhex-jira
   • Sawir ahaan
2. Waa maxay shaqada dhibcaha ee uu isticmaalo ColBERT?
   • MaxSim
   • Isku-dubarid cosine
   • Softmax cross-entropy
   • Baadhitaanka Beam
3. What query-time work does ColBERT avoid by precomputing document token vectors?
   • All query encoding and all index access
   • Every comparison between a query token and document token
   • The need to store document representations
   • Joint transformer encoding of every query-document pair, while query encoding and similarity search still run
4. Waa maxay macnaha 'daahida' ee 'isdhexgalka dambe'?
   • Qaabka waxaa loo tababaray goor dambe ee dhuumaha
   • Is dhexgalka u dhexeeya weydiimaha iyo dukumeentiyada dukumeentiga waxaa dib loogu dhigayaa tillaabada dhibcaha kadib codayn madax bannaan
   • Weydiimaha waxay yimaadaan goor dambe oo habeenkii ah
   • Dukumeentiyada ayaa lagu daraa ka dib marka la geeyo
5. Waa maxay dhibatada soo celinta cufan ee hal-vector ah ayuu ColBERT wax ka qabtaa?
   • Aad bay u gaabis tahay in la tilmaamo
   • Kuma shaqayn karo GPU-yada
   • Waxay u baahan tahay xog sumadeysan weydiin kasta
   • Isku-buufinta marin dhan oo loo galo hal vector waxay luminaysaa tafaasiil fiican
6. Muxuu ColBERTv2 si gaar ah uga horumaray kan asalka ahaa?
   • Waxay meesha ka saartay baahida loo qabo wax la isku dhejiyo
   • Waxay ka soo wareegtay BERT una wareegtay nidaam qaanuun ku salaysan
   • Waxay ku dartay isku-buufinta hadhaaga si ay si weyn u yarayso cabbirka tusmada
   • Waxay meesha ka saartay tallaabadii MaxSim
7. A two-token ColBERT query has best document-token similarities of 0.8 and 0.3. What MaxSim score results?
   • 0.8, retaining only the single largest value across the whole query
   • 1.1, the sum of the two per-query-token maxima
   • 0.55, averaging instead of summing the maxima
   • 0.24, multiplying the two maxima
8. Why can a ColBERT document index require more storage than a single-vector dense index?
   • It retains multiple token-level representations for each document
   • It never precomputes any document features
   • It must store all transformer gradients for each document
   • It removes every document token before indexing