{"id":13401,"date":"2024-07-14T14:53:31","date_gmt":"2024-07-14T14:53:31","guid":{"rendered":"https:\/\/dailyai.com\/?p=13401"},"modified":"2024-07-14T14:53:31","modified_gmt":"2024-07-14T14:53:31","slug":"ai-model-performance-is-it-reasoning-or-simply-reciting","status":"publish","type":"post","link":"https:\/\/dailyai.com\/nb\/2024\/07\/ai-model-performance-is-it-reasoning-or-simply-reciting\/","title":{"rendered":"AI-modellens ytelse: Resonnerer den eller resiterer den bare?"},"content":{"rendered":"

N\u00e5r ChatGPT gir deg det riktige svaret p\u00e5 sp\u00f8rsm\u00e5let ditt, resonnerer den seg gjennom foresp\u00f8rselen eller husker den bare svaret fra treningsdataene sine?<\/strong><\/p>\n

Forskere ved MITs Computer Science and Artificial Intelligence Laboratory (CSAIL) har utviklet en rekke tester for \u00e5 se om AI-modeller \"tenker\" eller bare har god hukommelse.<\/p>\n

N\u00e5r du ber en AI-modell om \u00e5 l\u00f8se en matematisk oppgave som \"Hva er 27+62?\", kommer den raskt tilbake med det riktige svaret: 89. Hvordan kan vi vite om den forst\u00e5r den underliggende aritmetikken eller om den bare s\u00e5 problemet i treningsdataene sine?<\/p>\n

I avisen deres<\/a>Forskerne testet GPT-4, GPT-3.5 Turbo, Claude 1.3 og PaLM2 for \u00e5 se om de kunne \"generalisere ikke bare til usette forekomster av kjente oppgaver, men ogs\u00e5 til nye oppgaver\".<\/p>\n

De utformet en serie p\u00e5 11 oppgaver som skilte seg litt fra standardoppgavene som LLM-ene generelt presterer godt i.<\/p>\n

LLM-ene b\u00f8r gj\u00f8re det like bra med de \"kontrafaktiske oppgavene\" hvis de bruker generelle og overf\u00f8rbare prosedyrer for oppgavel\u00f8sning.<\/p>\n

Hvis en LLM \"forst\u00e5r\" matematikk, b\u00f8r den for eksempel kunne gi riktig svar p\u00e5 en matematisk oppgave i base 10 og den sjeldent brukte base 9.<\/p>\n

Her ser du eksempler p\u00e5 oppgavene og GPT-4s ytelse.<\/p>\n

\"\"
GPT-4s prestasjoner med standard standardoppgaver (bl\u00e5) og lett endrede kontrafaktiske oppgaver (oransje). Eksempler p\u00e5 oppgavene og riktige svar vises her. Kilde: arXiv<\/figcaption><\/figure>\n

GPT-4s prestasjoner i standardtester (bl\u00e5 linje) er gode, men dens evner innen matematikk, logisk resonnering, romlig resonnering og andre evner (oransje linje) blir betydelig d\u00e5rligere n\u00e5r oppgaven endres noe.<\/p>\n

De andre modellene viste lignende nedbrytning, med GPT-4 p\u00e5 topp.<\/p>\n

Til tross for forringelsen var resultatene p\u00e5 kontrafaktiske oppgaver fortsatt bedre enn tilfeldighetene. AI-modellene pr\u00f8ver \u00e5 resonnere seg gjennom disse oppgavene, men er ikke s\u00e6rlig gode til det.<\/p>\n

Resultatene viser at AI-modellenes imponerende prestasjoner i oppgaver som h\u00f8yskoleeksamener er avhengig av utmerket gjenkalling av treningsdata, ikke resonnering. Dette understreker ytterligere at AI-modeller ikke kan generaliseres til usynlige oppgaver,<\/p>\n

Zhaofeng Wu, doktorgradsstudent i elektroteknikk og informatikk ved MIT, tilknyttet CSAIL og hovedforfatter av artikkelen, sier: \"Vi har avdekket et fascinerende aspekt ved store spr\u00e5kmodeller: De utmerker seg i kjente scenarier, nesten som en velbrukt sti, men sliter n\u00e5r terrenget blir ukjent. Denne innsikten er avgj\u00f8rende n\u00e5r vi jobber for \u00e5 forbedre modellenes tilpasningsevne og utvide bruksomr\u00e5dene deres.\"<\/p>\n

Vi s\u00e5 en lignende demonstrasjon av denne manglende evnen til \u00e5 generalisere da vi unders\u00f8kte hvor d\u00e5rlige AI-modeller er til \u00e5 l\u00f8se et forenklet puslespill for \u00e5 krysse en elv.<\/a><\/p>\n

Forskerne konkluderte med at n\u00e5r utviklere analyserer modellene sine, b\u00f8r de \"betrakte abstrakt oppgaveevne som l\u00f8srevet fra observert oppgaveutf\u00f8relse\".<\/p>\n

\"Tren-for-\u00e5-teste\"-tiln\u00e6rmingen kan f\u00e5 en modell til \u00e5 klatre i benchmarks, men gir ikke et reelt m\u00e5l p\u00e5 hvordan modellen vil klare seg n\u00e5r den blir presentert for en ny oppgave \u00e5 resonnere seg gjennom.<\/p>\n

Forskerne antyder at en del av problemet er at disse modellene kun er trent p\u00e5 overflatetekst.<\/p>\n

Hvis LLM-er blir eksponert for mer kontekstualiserte data og semantiske representasjoner fra den virkelige verden, vil de kanskje v\u00e6re i stand til \u00e5 generalisere n\u00e5r de blir presentert for oppgavevariasjoner.<\/p>","protected":false},"excerpt":{"rendered":"

N\u00e5r ChatGPT gir deg det riktige svaret p\u00e5 sp\u00f8rsm\u00e5let ditt, resonnerer den gjennom foresp\u00f8rselen eller husker den bare svaret fra treningsdataene sine? Forskere ved MITs Computer Science and Artificial Intelligence Laboratory (CSAIL) har designet en rekke tester for \u00e5 se om AI-modeller \"tenker\" eller bare har god hukommelse. N\u00e5r du ber en AI-modell om \u00e5 l\u00f8se et matematisk problem som \"Hva er 27+62?\", kommer den raskt tilbake med det riktige svaret: 89. Hvordan kan vi vite om den forst\u00e5r den underliggende aritmetikken eller bare s\u00e5 problemet i treningsdataene sine? I artikkelen testet forskerne GPT-4,<\/p>","protected":false},"author":6,"featured_media":13404,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[118],"class_list":["post-13401","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-llms"],"yoast_head":"\nAI model performance: Is it reasoning or simply reciting? | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/nb\/2024\/07\/ai-model-performance-is-it-reasoning-or-simply-reciting\/\" \/>\n<meta property=\"og:locale\" content=\"nb_NO\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI model performance: Is it reasoning or simply reciting? | DailyAI\" \/>\n<meta property=\"og:description\" content=\"When ChatGPT gives you the right answer to your prompt, does it reason through the request or simply remember the answer from its training data? MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers designed a series of tests to see if AI models \u201cthink\u201d or just have good memories. When you prompt an AI model to solve a math problem like \u201cWhat is 27+62?\u201d it comes back quickly with the correct answer: 89. How could we tell if it understands the underlying arithmetic or simply saw the problem in its training data? In their paper, the researchers tested GPT-4,\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dailyai.com\/nb\/2024\/07\/ai-model-performance-is-it-reasoning-or-simply-reciting\/\" \/>\n<meta property=\"og:site_name\" content=\"DailyAI\" \/>\n<meta property=\"article:published_time\" content=\"2024-07-14T14:53:31+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/dailyai.com\/wp-content\/uploads\/2024\/07\/AI-reasoning.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1792\" \/>\n\t<meta property=\"og:image:height\" content=\"1024\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"Eugene van der Watt\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DailyAIOfficial\" \/>\n<meta name=\"twitter:site\" content=\"@DailyAIOfficial\" \/>\n<meta name=\"twitter:label1\" content=\"Skrevet av\" \/>\n\t<meta name=\"twitter:data1\" content=\"Eugene van der Watt\" \/>\n\t<meta name=\"twitter:label2\" content=\"Ansl. lesetid\" \/>\n\t<meta name=\"twitter:data2\" content=\"3 minutter\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"NewsArticle\",\"@id\":\"https:\\\/\\\/dailyai.com\\\/2024\\\/07\\\/ai-model-performance-is-it-reasoning-or-simply-reciting\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/dailyai.com\\\/2024\\\/07\\\/ai-model-performance-is-it-reasoning-or-simply-reciting\\\/\"},\"author\":{\"name\":\"Eugene van der Watt\",\"@id\":\"https:\\\/\\\/dailyai.com\\\/#\\\/schema\\\/person\\\/7ce525c6d0c79838b7cc7cde96993cfa\"},\"headline\":\"AI model performance: Is it reasoning or simply reciting?\",\"datePublished\":\"2024-07-14T14:53:31+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/dailyai.com\\\/2024\\\/07\\\/ai-model-performance-is-it-reasoning-or-simply-reciting\\\/\"},\"wordCount\":532,\"publisher\":{\"@id\":\"https:\\\/\\\/dailyai.com\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/dailyai.com\\\/2024\\\/07\\\/ai-model-performance-is-it-reasoning-or-simply-reciting\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/dailyai.com\\\/wp-content\\\/uploads\\\/2024\\\/07\\\/AI-reasoning.webp\",\"keywords\":[\"LLMS\"],\"articleSection\":[\"Industry\"],\"inLanguage\":\"nb-NO\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/dailyai.com\\\/2024\\\/07\\\/ai-model-performance-is-it-reasoning-or-simply-reciting\\\/\",\"url\":\"https:\\\/\\\/dailyai.com\\\/2024\\\/07\\\/ai-model-performance-is-it-reasoning-or-simply-reciting\\\/\",\"name\":\"AI model performance: Is it reasoning or simply reciting? 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