quiz bu lëkkale ak tegtal · Diggante Niveau
Quiz buy boole ay ndawu liggéey
Xoolaat cër yi MoA di proposer ak aggregator, séddoo ay projet yu bari, njeexitalu liggéeyandoo, ak njëgu kompromis.
Laaj 1 de 8
Ci Njaxasu-Agent yi, lan mooy wareefu xeetu aggregatër bi?
Xoolaatal laaj bu nekk8 laaj
Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.
- Ci Njaxasu-Agent yi, lan mooy wareefu xeetu aggregatër bi?
- Dajale tontub xeetu proposer yi ci benn tontu bu gëna baax
- ngir efaase lépp ludul benn bordure ci anam wu mucc
- Tekki laaj bi ci kod
- Taggat xeetu proposer yi
- Ban moomeel bu am solo ci LLMs la këyitu MoA sukkandikoo ci?
- Ni LLMs mënu lire yeneen xeetu génnug
- Ni LLM yi di joxe tontu yu gëna baax suñu leen wane tontu seeni moroom ('jëflante')
- LLMs yi dañuy ànd saa yu nekk
- Ni model yu gëna rëy amul njariñ
- Lan mooy wuutale MoA ak wote yu bari yu yomb?
- Dafay tànn tontu li gëna bari luñu koy joxe baat par baat
- Benn model kese lay jëfandikoo
- Agrégateur bi dafay jàng bépp bordure ci anam wu muy tekki ba noppi defar ab synthese bu bees
- Dafay baña bàyyi xel ci ñi ko def
- Lan moo waral wuute ci xeetu xalaat yi am solo ci MoA?
- Dafay garanti génne yu nuróo
- Dafay wàññi limu diisaay yi ba amul dara
- Dafay juum ci tontu yépp
- Xeeti famiy model yu bari dañu bokk ci dooley aggregator bi mëna boole
- Lan mooy njëgu jëfu MoA bi gëna mag?
- Latency bu gëna rëy ak xayma, ndax couche bu nekk dafay yokk woote model
- Du soxla benn doggal
- Benn baat rek lay mëna génne
- Dafay efaase laaj bi njëkk
- Lan la këyitu MoA wax ci xeetu xët yu ubbeeku yi ci AlpacaEval 2.0?
- Mënu ñu woon doxal référence bi
- MoA bu model yu ubbeeku yi romb nañu poñ yi GPT-4 Omni am
- Modèle yu ubbeeku yi duñu def benn poñ
- Modèle yu tëju yi kese lañu natt
- What information is given to an agent in a later Mixture-of-Agents layer?
- Only the original prompt with all earlier drafts removed
- Only a majority-vote label
- The outputs produced by agents in the preceding layer
- A gradient update from each previous model
- What is the purpose of placing several proposer models in the first MoA layer?
- Train a shared base model from scratch
- Choose one response by majority vote and discard the rest
- Compress all outputs into a single token
- Create a set of candidate responses for later agents and the aggregator to use