Cross-Encoders vs Bi-Encoders Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Ndeupi mutsauko wekuvaka pakati pemuchinjiko-encoder uye bi-encoder?
   • Muchinjika-encoder unogadzira zvinyorwa zvese pamwechete kuti tokeni zvigone kupinda pazviri, ukuwo bi-encoder inokodha zvinyorwa zvese zvakasiyana.
   • Iyo bi-encoder inoshandisa kutarisa uye muchinjiko-encoder haishandise kutarisa
   • A cross-encoder idiki uye nekukurumidza kupfuura bi-encoder
   • Bi-encoder inokwanisa kubata chinyorwa chimwe chete panguva
2. Sei bi-encoders ichikwira kusvika kumamirioni emagwaro nemazvo?
   • Vanoshandisa data shoma yekudzidzisa
   • Zvinyorwa zvakamisikidzwa hazvina mubvunzo-zvakazvimirira uye zvinogona kuverengerwa nekuchengetwa
   • Havamboshandisi matransformer
   • Vanodarika nhanho inononoka yechiratidzo
3. Mune yakajairika pombi yekutsvaga yekutsvaga, iwo maviri ekuvaka anosanganiswa sei?
   • Cross-encoder inotora vavhoti, uye bi-encoder inovaisa zvakare
   • Ose ari maviri anomhanya achitevedzana uye mavhoti ari pakati
   • Bi-encoder inotora vakawanda vavhoti, kozoti muchinjika-encoder inodzokorodza iwo epamusoro
   • Imwe chete ndiyo yakamboshandiswa, kwete ese ari maviri
4. How does a late-interaction model such as ColBERT combine token-level query and document representations at scoring time?
   • It averages both texts into a single vector before any interaction
   • It concatenates raw audio waveforms and applies a vocoder
   • It ranks documents only by their character count
   • It compares query-token vectors with document-token vectors using a MaxSim-style aggregation
5. Chii chinoitwa nebi-encoder pamashoko ekuisa kamwe chete?
   • A single fixed-length embedding vector
   • Mucherechedzo wekuenderana pakati pezvinyorwa zviviri
   • Rondedzero yakarongwa yemagwaro
   • Mukana wekugovera pamusoro pemazwi
6. After jointly processing a query and candidate, what does a cross-encoder produce?
   • A reusable document vector computed independently of the query
   • A relevance score for that specific query-candidate pair
   • A separate token vector for every item in the full corpus
   • A guarantee that the top result is correct
7. What kind of representation does a late-interaction model such as ColBERT retain for a text?
   • Only one global label for the whole corpus
   • A decoded audio waveform
   • A vector representation for each token
   • A complete copy of the cross-encoder’s pairwise scores
8. What similarity function do Sentence Transformers examples commonly use for comparing normalized bi-encoder embeddings?
   • Edit distance over raw audio samples
   • A pixel-wise image loss
   • A speech-recognition word error rate
   • Cosine kufanana