{"id":2163,"date":"2023-06-30T21:15:16","date_gmt":"2023-06-30T21:15:16","guid":{"rendered":"https:\/\/dailyai.com\/?p=2163"},"modified":"2023-07-03T16:28:22","modified_gmt":"2023-07-03T16:28:22","slug":"anthropic-releases-paper-highlighting-the-potential-bias-of-large-language-models","status":"publish","type":"post","link":"https:\/\/dailyai.com\/nb\/2023\/06\/anthropic-releases-paper-highlighting-the-potential-bias-of-large-language-models\/","title":{"rendered":"Anthropic publiserer artikkel som avsl\u00f8rer skjevheten i store spr\u00e5kmodeller"},"content":{"rendered":"

En ny artikkel fra AI-selskapet Anthropic har belyst de potensielle skjevhetene som ligger i store spr\u00e5kmodeller (LLM-er), og antyder at disse AI-systemene kanskje ikke i tilstrekkelig grad representerer ulike globale perspektiver p\u00e5 samfunnssp\u00f8rsm\u00e5l.<\/strong><\/p>\n

Forskerne bygget opp et datasett, GlobalOpinionQA, som best\u00e5r av sp\u00f8rsm\u00e5l og svar fra internasjonale sp\u00f8rreunders\u00f8kelser utformet for \u00e5 fange opp ulike meninger om globale sp\u00f8rsm\u00e5l p\u00e5 tvers av landegrenser.\u00a0<\/span><\/p>\n

Anthropic's eksperimenter<\/span><\/a> spurte en LLM og fant ut at modellens svar som standard hadde en tendens til \u00e5 stemme bedre overens med holdningene til bestemte befolkningsgrupper, s\u00e6rlig fra USA, Storbritannia, Canada, Australia og noen f\u00e5 andre europeiske og s\u00f8ramerikanske land.\u00a0<\/span><\/p>\n

Slik fungerer det<\/span><\/h2>\n
    \n
  1. Opprettelse av datasett<\/b>: Teamet opprettet datasettet GlobalOpinionQA. Dette datasettet inneholder sp\u00f8rsm\u00e5l og svar fra unders\u00f8kelser p\u00e5 tvers av landegrenser som er spesielt utformet for \u00e5 fange opp et bredt spekter av meninger om globale sp\u00f8rsm\u00e5l.<\/span><\/li>\n
  2. Definere en likhetsmetrikk<\/b>: Deretter formulerte Anthropic en metrikk for \u00e5 m\u00e5le likheten mellom svarene gitt av LLM-er og menneskers svar. Denne beregningen tar hensyn til opprinnelseslandet til de menneskelige respondentene.<\/span><\/li>\n
  3. Oppl\u00e6ring av LLM<\/b>: Anthropic l\u00e6rte opp en LLM basert p\u00e5 \"konstitusjonell AI\", og s\u00f8rget for at LLM-en var hjelpsom, \u00e6rlig og ufarlig. Konstitusjonell AI er en teknikk utviklet av Anthropic som har som m\u00e5l \u00e5 gi AI-systemer \"verdier\" definert av en \"grunnlov\".<\/span><\/li>\n
  4. Gjennomf\u00f8ring av eksperimenter<\/b>: Ved hjelp av sitt n\u00f8ye utformede rammeverk utf\u00f8rte teamet hos Anthropic tre separate eksperimenter p\u00e5 den trente LLM-en.<\/span><\/li>\n<\/ol>\n

    Forskerne hevder at dette belyser potensielle skjevheter i modellene, noe som kan f\u00f8re til at visse gruppers meninger er underrepresentert sammenlignet med meninger fra vestlige land.\u00a0<\/span><\/p>\n

    De bemerket: \"Hvis en spr\u00e5kmodell i uforholdsmessig stor grad representerer visse meninger, risikerer den \u00e5 f\u00e5 potensielt u\u00f8nskede effekter, som \u00e5 fremme hegemoniske verdensbilder og homogenisere folks perspektiver og oppfatninger.\"<\/span><\/p>\n

    I tillegg observerte forskerne at det \u00e5 be modellen ta hensyn til et spesifikt lands perspektiv f\u00f8rte til svar som lignet mer p\u00e5 meningene til disse befolkningsgruppene. <\/span><\/p>\n

    Det betyr at du for eksempel kan be AI om \u00e5 \"vurdere det s\u00f8ramerikanske perspektivet\" p\u00e5 en bestemt kulturdebatt. <\/span>Noen ganger gjenspeilte svarene imidlertid skadelige kulturelle stereotypier, noe som tyder p\u00e5 at modellene mangler en nyansert forst\u00e5else av kulturelle verdier og perspektiver.<\/p>\n

    Det er interessant \u00e5 merke seg at n\u00e5r forskerne oversatte GlobalOpinionQA-sp\u00f8rsm\u00e5lene til m\u00e5lspr\u00e5ket, stemte ikke modellens svar n\u00f8dvendigvis overens med meningene til de som snakker disse spr\u00e5kene. <\/span><\/p>\n

    S\u00e5 det \u00e5 stille et sp\u00f8rsm\u00e5l p\u00e5 for eksempel japansk f\u00f8rte ikke n\u00f8dvendigvis til svar som var i tr\u00e5d med japanske kulturelle verdier. Du kan ikke \"skille\" AI-en fra dens overveiende vestlige verdier. <\/span><\/p>\n

    Dette tyder p\u00e5 at selv om LLM-ene er tilpasningsdyktige, m\u00e5 de tilegne seg en dypere forst\u00e5else av sosiale kontekster for \u00e5 kunne generere svar som gjenspeiler lokale meninger p\u00e5 en n\u00f8yaktig m\u00e5te.<\/span><\/p>\n

    Forskerne mener at funnene deres vil gi innsyn i perspektivene som er kodet og reflektert i dagens spr\u00e5kmodeller. Til tross for studiens begrensninger h\u00e5per de at den vil bidra til \u00e5 utvikle AI-systemer som gjenspeiler et mangfold av kulturelle synspunkter og erfaringer, ikke bare de som tilh\u00f8rer privilegerte eller dominerende grupper. De har ogs\u00e5 publisert datasettet sitt og en <\/span>interaktiv visualisering.<\/span><\/a><\/p>\n

    Denne studien er i stor grad i tr\u00e5d med andre akademiske arbeider om AIs sosiale og kulturelle verdier.<\/p>\n

    For det f\u00f8rste er de fleste grunnleggende AI-er oppl\u00e6rt av hovedsakelig vestlige selskaper og forskerteam.<\/p>\n

    I tillegg er data som brukes til \u00e5 trene opp kunstig intelligens<\/a> ikke alltid representerer samfunnet som helhet. For eksempel er de aller fleste oppl\u00e6ringsdataene for LLM-er skrevet p\u00e5 engelsk, noe som sannsynligvis gjenspeiler engelskspr\u00e5klige samfunnsmessige og kulturelle verdier.<\/p>\n

    Forskere er godt kjent med potensielle skjevheter og diskriminering i kunstig intelligens. Det er imidlertid ekstremt komplekst \u00e5 l\u00f8se dette problemet, og det krever en n\u00f8ye blanding av tilpassede datasett av h\u00f8y kvalitet og flittig menneskelig input og overv\u00e5king.<\/p>","protected":false},"excerpt":{"rendered":"

    En ny artikkel fra AI-selskapet Anthropic har belyst de potensielle skjevhetene som ligger i store spr\u00e5kmodeller (LLM-er), og antyder at disse AI-systemene kanskje ikke i tilstrekkelig grad representerer ulike globale perspektiver p\u00e5 samfunnssp\u00f8rsm\u00e5l. Forskerne bygget et datasett, GlobalOpinionQA, som best\u00e5r av sp\u00f8rsm\u00e5l og svar fra internasjonale sp\u00f8rreunders\u00f8kelser utformet for \u00e5 fange opp ulike meninger om globale sp\u00f8rsm\u00e5l p\u00e5 tvers av landegrenser. Anthropics eksperimenter testet en LLM-modell og fant ut at modellens svar som standard hadde en tendens til \u00e5 ligge tettere opp til meningene til bestemte befolkningsgrupper, s\u00e6rlig fra USA, Storbritannia, Canada, Australia og noen f\u00e5 andre europeiske og s\u00f8ramerikanske land. Hvordan<\/p>","protected":false},"author":2,"featured_media":2164,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[88],"tags":[148,118],"class_list":["post-2163","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ethics","tag-anthropic","tag-llms"],"yoast_head":"\nAnthropic releases paper revealing the bias of large language models | DailyAI<\/title>\n<meta name=\"description\" content=\"A new paper by AI company Anthropic has shed light on the potential biases inherent in large language models (LLMs), suggesting these AI systems may not adequately represent diverse global perspectives on societal issues.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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