{"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\/nb\/2023\/06\/new-mit-self-learning-language-models-can-beat-other-llms\/","title":{"rendered":"Nye selvl\u00e6rende spr\u00e5kmodeller fra MIT kan sl\u00e5 andre LLM-er"},"content":{"rendered":"

I l\u00f8pet av de siste seks m\u00e5nedene har vi sett en revolusjonerende boom av <\/span>AI-drevet<\/span><\/a> LLM-er (Large Language Models) st\u00e5r i sentrum. Men er det alltid n\u00f8dvendig for et AI-produkt eller en AI-tjeneste \u00e5 v\u00e6re basert p\u00e5 LLM-er? If\u00f8lge en artikkel er de nye selvl\u00e6rende spr\u00e5kmodellene fra MIT ikke basert p\u00e5 LLM-er, og de kan utkonkurrere noen av de andre store AI-systemene som for tiden leder an i bransjen.<\/span><\/p>\n

En gruppe forskere ved MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) har utviklet en ny m\u00e5te \u00e5 tiln\u00e6rme seg AI-spr\u00e5kmodeller p\u00e5.<\/span><\/p>\n

Det er en banebrytende prestasjon som legger vekt p\u00e5 mindre spr\u00e5kmodeller og deres evne til \u00e5 l\u00f8se problemer med ineffektivitet, samt personvernhensyn som er knyttet til utviklingen av store AI-modeller basert p\u00e5 tekstdata.<\/span><\/p>\n

Med fremveksten av <\/span>OpenAIs<\/span><\/a> ChatGPT basert p\u00e5 spr\u00e5kmodellene GPT-3 og GPT-4, har mange selskaper sluttet seg til AI-kappl\u00f8pet, inkludert Google Bard og andre <\/span>generativ AI<\/span><\/a> systemer som gj\u00f8r det mulig \u00e5 generere tekst, bilder og til og med videoer.<\/span><\/p>\n

For \u00e5 generere resultater av up\u00e5klagelig kvalitet er disse systemene imidlertid avhengige av mye data som det er dyrt \u00e5 behandle beregningsmessig. Mange av disse systemene importerer data for oppl\u00e6ring via API-er, noe som medf\u00f8rer risikoer som datalekkasjer og andre personvernproblemer.<\/span><\/p>\n

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

If\u00f8lge en ny artikkel med tittelen<\/span> Entailment som robuste selvl\u00e6rere<\/span><\/a> I en ny artikkel som n\u00e5 er publisert i det elektroniske arkivet arXiv, viser forskerne at nye selvl\u00e6rende spr\u00e5kmodeller fra MIT kan l\u00f8se problemet med \u00e5 forst\u00e5 visse spr\u00e5koppgaver som store spr\u00e5kmodeller har. De kaller denne banebrytende prestasjonen for tekstlig entailment.<\/span><\/p>\n

Modellene er basert p\u00e5 konseptet om at hvis det finnes to setninger - en premiss og en hypotese - og premisset i den f\u00f8rste setningen er sant, er det sannsynlig at hypotesen ogs\u00e5 er sann.<\/span><\/p>\n

I en<\/span> uttalelse<\/span><\/a> publisert p\u00e5 MIT CSAIL-bloggen, kan et eksempel p\u00e5 denne strukturen v\u00e6re at hvis \"alle katter har hale\", er det sannsynlig at hypotesen \"en tabby katt har hale\" er sann. Denne tiln\u00e6rmingen f\u00f8rer til mindre bias i <\/span>AI-modeller<\/span><\/a>, noe som gj\u00f8r at de nye selvl\u00e6rende spr\u00e5kmodellene fra MIT utkonkurrerer st\u00f8rre spr\u00e5kmodeller, if\u00f8lge uttalelsen.<\/span><\/p>\n

\"V\u00e5re selvtrente, 350 millioner parametere, uten menneskeskapte etiketter, utkonkurrerer overv\u00e5kede spr\u00e5kmodeller med 137 til 175 milliarder parametere\", sier MIT CSAIL Postdoctoral Associate Hongyin Luo, hovedforfatter i en uttalelse. \"<\/span><\/p>\n

Han la ogs\u00e5 til at denne tiln\u00e6rmingen kan v\u00e6re sv\u00e6rt gunstig for dagens AI-systemer og omforme maskinl\u00e6ringssystemer p\u00e5 en m\u00e5te som er mer skalerbar, p\u00e5litelig og kostnadseffektiv n\u00e5r man jobber med spr\u00e5kmodeller.<\/span><\/p>\n

Nye selvl\u00e6rende spr\u00e5kmodeller fra MIT er fortsatt begrensede<\/h2>\n

Selv om de nye selvl\u00e6rende spr\u00e5kmodellene fra MIT lover mye n\u00e5r det gjelder \u00e5 l\u00f8se bin\u00e6re klassifiseringsproblemer, er de fortsatt begrenset til \u00e5 l\u00f8se flerklasseklassifiseringsproblemer. Det betyr at den tekstlige sammenhengen ikke fungerer like godt n\u00e5r modellen blir presentert for flere valg.<\/span><\/p>\n

If\u00f8lge James Glass, MIT-professor og CSAIL Principal Investigator, som ogs\u00e5 har forfattet artikkelen, kan denne forskningen kaste lys over effektive metoder for \u00e5 l\u00e6re opp LLM-er til \u00e5 forst\u00e5 kontekstuelle problemer.<\/span><\/p>\n

\"Selv om LLM-feltet gjennomg\u00e5r raske og dramatiske endringer, viser denne forskningen at det er mulig \u00e5 produsere relativt kompakte spr\u00e5kmodeller som presterer sv\u00e6rt godt p\u00e5 referanseforst\u00e5elsesoppgaver sammenlignet med tilsvarende modeller av omtrent samme st\u00f8rrelse, eller til og med mye st\u00f8rre spr\u00e5kmodeller\", sier han.<\/span><\/p>\n

Denne forskningen er bare begynnelsen p\u00e5 fremtidige AI-teknologier som kan l\u00e6re av seg selv og v\u00e6re mer effektive, b\u00e6rekraftige og fokuserte p\u00e5 personvern. Artikkelen om de nye selvl\u00e6rende spr\u00e5kmodellene fra MIT vil bli presentert i juli p\u00e5 m\u00f8tet til Association for Computational Linguistics i Toronto. Prosjektet st\u00f8ttes ogs\u00e5 av<\/span> Hong Kong Innovation AI-program<\/span><\/a>.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

I l\u00f8pet av de siste seks m\u00e5nedene har vi sett en revolusjonerende boom av AI-drevne LLM-er (Large Language Models). Men er det alltid n\u00f8dvendig for et AI-produkt eller en AI-tjeneste \u00e5 v\u00e6re basert p\u00e5 LLM-er? If\u00f8lge en artikkel er nye selvl\u00e6rende spr\u00e5kmodeller fra MIT ikke basert p\u00e5 LLM-er, og de kan utkonkurrere noen av de andre store AI-systemene som for tiden leder an i bransjen. En gruppe forskere ved MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) har utviklet en ny m\u00e5te \u00e5 tiln\u00e6rme seg AI-spr\u00e5kmodeller p\u00e5. Det er en banebrytende prestasjon som legger vekt p\u00e5 mindre spr\u00e5kmodeller og deres evne til \u00e5 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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