{"id":1278,"date":"2023-06-05T17:43:13","date_gmt":"2023-06-05T17:43:13","guid":{"rendered":"https:\/\/dailyai.com\/?p=1278"},"modified":"2023-06-05T17:43:13","modified_gmt":"2023-06-05T17:43:13","slug":"new-mit-self-learning-language-models-can-beat-other-llms","status":"publish","type":"post","link":"https:\/\/dailyai.com\/da\/2023\/06\/new-mit-self-learning-language-models-can-beat-other-llms\/","title":{"rendered":"Nye selvl\u00e6rende sprogmodeller fra MIT kan sl\u00e5 andre LLM'er"},"content":{"rendered":"

I l\u00f8bet af de sidste seks m\u00e5neder har vi set et revolutionerende boom af <\/span>AI-drevet<\/span><\/a> LLM'er (Large Language Models) er i centrum. Men er det altid n\u00f8dvendigt, at et AI-produkt eller en AI-tjeneste er baseret p\u00e5 LLM'er? If\u00f8lge en artikel er nye selvl\u00e6rende sprogmodeller fra MIT ikke baseret p\u00e5 LLM'er og kan udkonkurrere nogle af de andre store AI-systemer, der i \u00f8jeblikket er f\u00f8rende i branchen.<\/span><\/p>\n

En gruppe forskere ved MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) har udviklet en ny m\u00e5de at gribe AI-sprogmodeller an p\u00e5.<\/span><\/p>\n

Det er en banebrydende pr\u00e6station, der l\u00e6gger v\u00e6gt p\u00e5 mindre sprogmodeller og deres evne til at l\u00f8se problemer med ineffektivitet samt bekymringer om privatlivets fred, der er forbundet med udviklingen af store AI-modeller baseret p\u00e5 tekstdata.<\/span><\/p>\n

Med fremkomsten af <\/span>OpenAI's<\/span><\/a> ChatGPT er baseret p\u00e5 sprogmodellerne GPT-3 og GPT-4, og mange virksomheder deltog i AI-kapl\u00f8bet, herunder Google Bard og andre. <\/span>generativ AI<\/span><\/a> systemer, der giver folk mulighed for at generere tekst, billeder og endda videoer.<\/span><\/p>\n

Men for at generere output af up\u00e5klagelig kvalitet er disse systemer afh\u00e6ngige af en masse data, som er dyre at behandle beregningsm\u00e6ssigt. Mange af disse systemer importerer data til tr\u00e6ning via API'er, hvilket medf\u00f8rer sine egne risici s\u00e5som datal\u00e6kager og andre bekymringer om privatlivets fred.<\/span><\/p>\n

Tekstuel konsekvens<\/b><\/h2>\n

If\u00f8lge en ny artikel med titlen<\/span> Entailment som robuste selvl\u00e6rere<\/span><\/a> I en ny artikel, som i \u00f8jeblikket er offentliggjort i preprinting online repository arXiv, bem\u00e6rker forskerne, at nye selvl\u00e6rende sprogmodeller fra MIT kan l\u00f8se problemet med at forst\u00e5 visse sprogopgaver, som store sprogmodeller har. De kalder denne banebrydende pr\u00e6station for tekstuel entailment.<\/span><\/p>\n

Modellerne er baseret p\u00e5 det koncept, at hvis der er to s\u00e6tninger - en pr\u00e6mis og en hypotese - og hvis en pr\u00e6mis i den f\u00f8rste s\u00e6tning er sand, s\u00e5 er hypotesen sandsynligvis ogs\u00e5 sand.<\/span><\/p>\n

I en<\/span> erkl\u00e6ring<\/span><\/a> offentliggjort p\u00e5 MIT CSAIL-bloggen, ville et eksempel p\u00e5 denne struktur v\u00e6re, at hvis \"alle katte har haler\", er det sandsynligt, at hypotesen \"en tabby-kat har en hale\" er sand. Denne tilgang f\u00f8rer til mindre bias i <\/span>AI-modeller<\/span><\/a>hvilket g\u00f8r, at de nye selvl\u00e6rende sprogmodeller fra MIT udkonkurrerer st\u00f8rre sprogmodeller if\u00f8lge udtalelsen.<\/span><\/p>\n

\"Vores selvtr\u00e6nede entailment-modeller med 350 millioner parametre uden menneskeskabte etiketter overg\u00e5r overv\u00e5gede sprogmodeller med 137 til 175 milliarder parametre,\" siger MIT CSAIL Postdoctoral associate Hongyin Luo, hovedforfatter, i en udtalelse. \"<\/span><\/p>\n

Han tilf\u00f8jede ogs\u00e5, at denne tilgang kunne v\u00e6re meget gavnlig for nuv\u00e6rende AI-systemer og omforme maskinl\u00e6ringssystemer p\u00e5 en m\u00e5de, der er mere skalerbar, p\u00e5lidelig og omkostningseffektiv, n\u00e5r man arbejder med sprogmodeller.<\/span><\/p>\n

Nye selvl\u00e6rende sprogmodeller fra MIT er stadig begr\u00e6nsede<\/h2>\n

Selvom de nye selvl\u00e6rende sprogmodeller fra MIT lover meget, n\u00e5r det g\u00e6lder om at l\u00f8se bin\u00e6re klassifikationsproblemer, er de stadig begr\u00e6nsede til at l\u00f8se klassifikationsproblemer i flere klasser. Det betyder, at den tekstuelle konsekvens ikke fungerer lige s\u00e5 godt, n\u00e5r modellen pr\u00e6senteres for flere valgmuligheder.<\/span><\/p>\n

If\u00f8lge James Glass, MIT-professor og CSAIL Principal Investigator, som ogs\u00e5 er forfatter til artiklen, kan denne forskning kaste lys over effektive metoder til at tr\u00e6ne LLM'er i at forst\u00e5 kontekstuelle problemer.<\/span><\/p>\n

\"Mens LLM-feltet underg\u00e5r hurtige og dramatiske forandringer, viser denne forskning, at det er muligt at producere relativt kompakte sprogmodeller, der klarer sig meget godt i benchmark-forst\u00e5elsesopgaver sammenlignet med deres j\u00e6vnaldrende af nogenlunde samme st\u00f8rrelse eller endda meget st\u00f8rre sprogmodeller\", siger han.<\/span><\/p>\n

Denne forskning er kun begyndelsen p\u00e5 fremtidige AI-teknologier, der kan l\u00e6re af sig selv og v\u00e6re mere effektive, b\u00e6redygtige og fokuserede p\u00e5 databeskyttelse. Artiklen om de nye selvl\u00e6rende sprogmodeller fra MIT vil blive pr\u00e6senteret i juli p\u00e5 m\u00f8det i Association for Computational Linguistics i Toronto. Projektet st\u00f8ttes ogs\u00e5 af<\/span> Hong Kong Innovation AI-program<\/span><\/a>.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

I l\u00f8bet af de sidste seks m\u00e5neder har vi set et revolutionerende boom af AI-drevne LLM'er (Large Language Models) indtage scenen. Men er det altid n\u00f8dvendigt, at et AI-produkt eller en tjeneste er baseret p\u00e5 LLM'er? If\u00f8lge en artikel er nye selvl\u00e6rende sprogmodeller fra MIT ikke baseret p\u00e5 LLM'er og kan udkonkurrere nogle af de andre store AI-systemer, der i \u00f8jeblikket er f\u00f8rende i branchen. En gruppe forskere ved MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) har udviklet en ny m\u00e5de at gribe AI-sprogmodeller an p\u00e5. Det er en banebrydende pr\u00e6station, der l\u00e6gger v\u00e6gt p\u00e5 mindre sprogmodeller og deres evne til at adressere<\/p>","protected":false},"author":4,"featured_media":1280,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[118,117],"class_list":["post-1278","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-llms","tag-mit"],"yoast_head":"\nNew MIT self-learning language models can beat other LLMs | DailyAI<\/title>\n<meta name=\"description\" content=\"MIT self-learning language models are not based on LLMs and can outperform some of the other large AI systems that currently lead the industry\" \/>\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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Simic","image":{"@type":"ImageObject","inLanguage":"da-DK","@id":"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/06\/danica-pp3-96x96.jpeg","url":"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/06\/danica-pp3-96x96.jpeg","contentUrl":"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/06\/danica-pp3-96x96.jpeg","caption":"Danica Simic"},"description":"Danica er data scientist med mere end 7 \u00e5rs erfaring inden for AI og Big Data. Hun har produceret specialiseret indhold for bl.a. ValueWalk og IntegrateAI og har skrevet forskningsartikler for Singidunum Univeristy.","sameAs":["https:\/\/www.linkedin.com\/in\/dasimic\/?originalSubdomain=rs"],"url":"https:\/\/dailyai.com\/da\/author\/danicasimic\/"}]}},"_links":{"self":[{"href":"https:\/\/dailyai.com\/da\/wp-json\/wp\/v2\/posts\/1278","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dailyai.com\/da\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dailyai.com\/da\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dailyai.com\/da\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/dailyai.com\/da\/wp-json\/wp\/v2\/comments?post=1278"}],"version-history":[{"count":3,"href":"https:\/\/dailyai.com\/da\/wp-json\/wp\/v2\/posts\/1278\/revisions"}],"predecessor-version":[{"id":1376,"href":"https:\/\/dailyai.com\/da\/wp-json\/wp\/v2\/posts\/1278\/revisions\/1376"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dailyai.com\/da\/wp-json\/wp\/v2\/media\/1280"}],"wp:attachment":[{"href":"https:\/\/dailyai.com\/da\/wp-json\/wp\/v2\/media?parent=1278"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dailyai.com\/da\/wp-json\/wp\/v2\/categories?post=1278"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dailyai.com\/da\/wp-json\/wp\/v2\/tags?post=1278"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}