Maswali Mzito ya Urejeshaji wa Kifungu

Majibu na maelezo huonekana unapojibu maswali.

1. Je, Urejeshaji wa Kifungu Mnene huamua vipi vifungu vinavyolingana na swali?
   • Kulinganisha uwakilishi wa vekta mnene wa maswali na vifungu kwa kufanana
   • Kuhesabu ni maneno mangapi ya swali yanayoonekana katika kila hati
   • Kutafsiri swali katika SQL kabla ya kutafuta
   • Kuorodhesha hati kulingana na jinsi zilivyochapishwa hivi majuzi
2. DPR hutumia usanifu wa 'bi-encoder'. Hiyo ina maana gani?
   • Inasimba maandishi mara mbili na wastani wa matokeo
   • Inafanya kazi kwenye hati za lugha mbili pekee
   • Inasimba herufi na maneno kwa wakati mmoja
   • Inatumia encoders mbili tofauti, moja kwa maswali na moja kwa vifungu
3. Kwa nini veta za kifungu zinaweza kukokotwa mapema katika DPR?
   • Kwa sababu vifungu havibadiliki mara moja yameandikwa
   • Kwa sababu kisimbaji cha hoja kimezimwa wakati wa utafutaji
   • Kwa sababu kila kifungu kimesimbwa bila ya swali lolote
   • Kwa sababu vifungu vinahifadhiwa kama maandishi ghafi pekee
4. Ni mbinu gani ya mafunzo huruhusu DPR kutoa mifano mingi hasi kwa bei nafuu?
   • Kugeuza lebo bila mpangilio
   • Hasi katika kundi, kwa kutumia vifungu vingine katika kundi moja
   • Kuacha nusu ya neurons
   • Kuweka lebo kwa kila kifungu kisicho sahihi
5. For what vocabulary mismatch can learned DPR representations be useful compared with lexical matching alone?
   • The passage is unrelated but repeats the query's terms
   • Every query contains only an exact serial number
   • The encoder is guaranteed to retrieve the right answer for all inputs
   • A relevant passage expresses a query's idea using different words
6. Je, ni zana gani inayotumiwa sana kufanya utafutaji wa jirani wa karibu wa DPR haraka kwa kiwango?
   • Faharasa ya takriban ya jirani kama vile FAISS
   • Aina ya lahajedwali
   • Injini ya kujieleza ya kawaida
   • Kujiunga na hifadhidata ya uhusiano
7. Why can BM25-retrieved nonanswer passages be useful hard negatives when training DPR?
   • They guarantee that every retrieved passage is irrelevant
   • Their lexical overlap can make them challenging distractors that the retriever must distinguish from the answer passage
   • They remove the need for a positive answer passage
   • They force the passage encoder to count words without learning
8. After changing DPR's passage encoder, what should happen to a stored passage-vector index?
   • Keep the old embeddings because encoder weights cannot affect vectors
   • Rename the index without recalculating any representation
   • Recompute the passage embeddings with the intended compatible encoder and update the index
   • Disable the query encoder and search raw filenames instead