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HyDE waxay wanaajisaa dib u soo celinta iyadoo marka hore waydiisa qaabka luqadda si uu u qiyaaso dukumeenti jawaabeed been abuur ah, ka dibna ku raadisa dukumeentiga guntiisa bedelka su'aasha cayriin.
Dhageyso hagahanAI-waa laga sheekeeyay · ~2 min
Waxay isku xidhaa farqiga u dhexeeya su'aalaha gaagaaban iyo tuducyada dhaadheer ee aad dhab ahaantii rabto inaad hesho.
HyDE was proposed by Gao and colleagues in 2022 for dense retrieval without relevance labels. It asks an instruction-following language model to generate a hypothetical answer document, embeds that text with an unsupervised contrastive encoder such as Contriever, and searches the actual corpus for similar vectors. The hypothetical document can supply document-like topical patterns even when its details are false. The paper describes the dense encoder as a bottleneck that can reduce the influence of invented details; this is not a guarantee that errors never affect retrieval. Evaluate real retrieved passages rather than treating the generated text as factual evidence. HyDE targets a query-to-document mismatch and reported strong results on tested tasks and languages, without implying universal superiority over trained retrievers.
Dib-u-soo-celinta eber-ku-soo-noqoshada ee goob cusub oo aan lahayn calaamad sumadeed xogta tababarka-mareegta
Raadinta luqado badan, soo saarida jawaab mala awaal ah luqadda bartilmaameedka ka hor inta aan la dhex gelin
Hagaajinta xusuusta RAG iyada oo la balaadhinayo su'aalaha isticmaalaha isticmaala waraaqo been abuur ah oo qani ah
Cilmi baaris iyo sharci raadin halka su'aalaha gaagaaban ay u baahan yihiin inay iswaafaqaan cufan, tuducyo il-culus oo jargon-culus
HyDE waa dhisme ku jira dhuumaha RAG ee horumarsan, oo inta badan lagu daro dib u habeyn iyo jiil su'aalo badan. Filo kala duwanaansho soo saara dukumiintiyo mala awaal ah oo celcelis ahaan ku dhejistooda si adag, isticmaalka la qabsiga ee kiciya HyDE kaliya marka su'aasha cayriin ay si liidata u soo ceshato, iyo la dhexgalka adag ee LLM-yada maxalliga ah ee jaban si loo yareeyo daahitaanka iyo qiimaha. Marka moodooyinka wax-soo-saarka ahi ay horumaraan, tayada dukumentiyada mala-awaalka ah - oo sidaas darteed dib-u-soo-celinta - waa inay sii kordheysaa.
Warqad bilaash ah
Saddex sheeko oo la xaqiijiyay oo AI ah maalin kasta oo shaqo ah, oo lagu qoray Ingiriis fudud. Bilaash had iyo jeer, aan xayaysiis lahayn.
Hal iimayl maalin kasta oo shaqo ah. Ka bax hal gujin. Marna ma iibinno ama ma wadaagno ciwaankaaga.
Is tijaabi
Fikrad degdeg ah oo ku saabsan jawaab kasta, iyo shahaado la wadaajin karo oo leh aqoonsi la xaqiijin karo markaad ka gudubto koorso.
Taageer waxbarashada AI bilaashka ah. AI Understanding waa 501(c)(3) aan faa'iido doon ahayn — xayaysiis la'aan, albaab lacag ah ma jiro, had iyo jeer. Samee deeq
HyDE waxay wanaajisaa dib u soo celinta iyadoo marka hore waydiisa qaabka luqadda si uu u qiyaaso dukumeenti jawaabeed been abuur ah, ka dibna ku raadisa dukumeentiga guntiisa bedelka su'aasha cayriin. Waxay isku xidhaa farqiga u dhexeeya su'aalaha gaagaaban iyo tuducyada dhaadheer ee aad dhab ahaantii rabto inaad hesho.
HyDE waxay ku dhiirigelisaa LLM inuu qoro dukumeenti jawaabeed mala awaal ah, kaas oo guntintiisa loo isticmaalo raadinta marinnada dhabta ah.
The paper motivates the dense bottleneck as filtering details, but a generated error can still change the representation and retrieved results.
HyDE waxay ku lammaanaysaa koronto dhaliyaha LLM iyo codeeyaha isbar-bardhigga ah ee aan la ilaalinayn sida Contriever si loo dhejiyo dukumeentiga mala-awaalka ah.
Adiga oo su'aasha u rogaya qoraal u eg dukumeenti, HyDE waxay u dhaqaajinaysaa ra'yiga raadinta u dhow nooca tuducyada ay tahay inay la mid noqoto.
Combining embeddings is one way to aggregate generated samples; it does not guarantee improved retrieval on every query.
Sii wad waxbarashada
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