Is-RAG iyo Kediska Soo Celinta Milicsiga

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Compared with a baseline that retrieves for every input, what adaptive decision does Self-RAG learn?
   • Which operating system to install for every query
   • Whether retrieval is needed, including decisions as generation progresses
   • How to permanently remove all document sources
   • How to prove every generated statement without inspecting evidence
2. Maxay yihiin 'calaamadaha milicsiga' ee Self-RAG?
   • Furayaasha sirta ah ee dukumentiyada
   • Aqoonsiga gelitaanka isticmaalaha
   • Jadwalka waqtiga ee faylasha la soo saaray
   • Calaamadaha kontoroolka gaarka ah ee moodeelku soo saaro si uu u xukumo ku habboonaanta, taageerada, iyo faa'iidada
3. What does Self-RAG's IsSupported reflection token predict?
   • Whether the model's hardware has enough disk space
   • Whether a support ticket has been paid
   • Whether a generated segment is supported by the retrieved evidence
   • Whether every source claim is independently proven true
4. Sidee buu mooda-RAG caadi ahaan loogu tababbaray inuu soo saaro calaamadaha milicsiga?
   • Barashada korjoogteynta ee xogta lagu calaamadeeyay calaamadaha milicsiga, oo inta badan laga soocay qaab adag
   • Kutlada aan si toos ah loo ilaalin
   • Xeerarka gacanta lagu codeeyo oo aan wax barasho lahayn
   • Kaliya iyadoo la xoojinayo barashada bini'aadamka qasabnada
5. Faa'iido intee le'eg ayay leedahay soo saarista 'IsRelevant' ee tuduc kasta?
   • Waxay dedejisaa isku xirka shabakada
   • Waxay cadaadisaa dukumeentiga
   • Waxay beddeshaa tokenizer model
   • Waxay miiraysaa tuducyada la soo saaray ee aan run ahaantii caawinayn waydiinta
6. How can reflection-token scores affect Self-RAG inference without a new training run?
   • Every query must update all model parameters
   • Weighted critique scores can be combined with generation likelihood during segment-level candidate search
   • The critique scores must replace all source text with random tokens
   • Changing a score weight guarantees all factual errors disappear
7. What should an evaluator infer from a high Self-RAG IsSupported probability alone?
   • The model predicts support, which still needs checking against the actual evidence
   • The answer has been independently verified as true
   • No source passage needs to be read
   • Every external document is guaranteed accurate
8. At what granularity can Self-RAG adapt retrieval while developing a response?
   • Only when the operating system restarts
   • Only after the final answer has been published
   • As generated segments progress, rather than only one fixed retrieval at the beginning
   • Only by changing the tokenizer's vocabulary permanently