Seetug Semantik

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

1. Lan mooy wuutale retriever semantik bu dëgër buñu ko méngale ak baseline lexical bu am poñ yu sell?
   • Every lexical system can match only identical raw bytes
   • Dense retrieval guarantees a correct answer for every paraphrase
   • It ranks using learned vector relationships rather than only term-based evidence
   • Semantic retrieval must ignore the query
2. naka la ko seetlu bu dëgër mëna joxee poñ ci laaj bi ci benn passage?
   • By comparing compatible query and passage vectors using its trained metric
   • Only by sorting passage filenames
   • Only by taking the latest publication date
   • By requiring identical raw text in every result
3. Lan moo waral seetlu bu dëgër mën na jëfandikoo limu HNSW?
   • To guarantee exact top results for every setting
   • To prove every neighbor is factually correct
   • To eliminate the need to encode searchable items
   • To reduce search work while accepting a possible nearest-neighbor recall tradeoff
4. Ban xayma mooy wuutale etape de ranking encodeur cross?
   • Only offline encoding of the passage without its query
   • Joint evaluation of the query with each selected candidate
   • Retraining the whole corpus encoder for every result
   • Removing all candidates before evaluating relevance
5. Luy sistemu seetlu 'hybrid'?
   • Sistem buy boole poñ vecteur semantik ak poñ baatu-caabi bu yàgg
   • Sistem buy dox ci telefon yi ak serwër yi
   • Sistem buy seetee nataal ak audio kese
   • Sistem bu musul jëfandikoo ay samp
6. In a RAG system using semantic retrieval, what does the retriever provide to the generator?
   • A guarantee that every final answer is true
   • Newly trained generator weights for each question
   • Selected source passages as context
   • A replacement for all response generation
7. In the original DPR inference procedure, which representations are computed and indexed offline?
   • Vectors for every possible future question
   • Passage vectors for the searchable corpus
   • Final generated answers for every future user
   • Only scores from jointly processing unknown query-passage pairs
8. What relationship does DPR’s training objective encourage for a question and its positive passage relative to negative passages?
   • An identical score for all positives and negatives
   • A lower positive score than every negative
   • Alphabetical ranking independent of encoder outputs
   • A higher dot-product score for the positive passage