{"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\/da\/2024\/07\/ai-model-performance-is-it-reasoning-or-simply-reciting\/","title":{"rendered":"AI-modellers ydeevne: R\u00e6sonnerer den eller reciterer den bare?"},"content":{"rendered":"

N\u00e5r ChatGPT giver dig det rigtige svar p\u00e5 din foresp\u00f8rgsel, r\u00e6sonnerer den s\u00e5 gennem foresp\u00f8rgslen, eller husker den blot svaret fra sine tr\u00e6ningsdata?<\/strong><\/p>\n

Forskere fra MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) har designet en r\u00e6kke tests for at se, om AI-modeller \"t\u00e6nker\" eller bare har en god hukommelse.<\/p>\n

N\u00e5r du beder en AI-model om at l\u00f8se et matematisk problem som \"Hvad er 27+62?\", kommer den hurtigt tilbage med det rigtige svar: 89. Hvordan kan vi vide, om den forst\u00e5r den underliggende aritmetik eller blot har set problemet i sine tr\u00e6ningsdata?<\/p>\n

I deres papir<\/a>Forskerne testede GPT-4, GPT-3.5 Turbo, Claude 1.3 og PaLM2 for at se, om de kunne \"generalisere ikke kun til usete forekomster af kendte opgaver, men ogs\u00e5 til nye opgaver.\"<\/p>\n

De designede en serie p\u00e5 11 opgaver, der adskilte sig en smule fra de standardopgaver, som LLM'erne generelt klarer sig godt i.<\/p>\n

LLM'erne burde klare sig lige s\u00e5 godt med de \"kontrafaktiske opgaver\", hvis de anvender generelle og overf\u00f8rbare procedurer for opgavel\u00f8sning.<\/p>\n

Hvis en LLM \"forst\u00e5r\" matematik, b\u00f8r den give det korrekte svar p\u00e5 et matematisk problem i base-10 og den sj\u00e6ldent anvendte base-9, for eksempel.<\/p>\n

Her er et kig p\u00e5 eksempler p\u00e5 opgaverne og GPT-4's pr\u00e6station.<\/p>\n

\"\"
GPT-4's pr\u00e6station med standardopgaver (bl\u00e5) og let \u00e6ndrede kontrafaktiske opgaver (orange). Eksempler p\u00e5 opgaverne og de korrekte svar er vist her. Kilde: arXiv<\/figcaption><\/figure>\n

GPT-4's pr\u00e6station i standardtests (bl\u00e5 linje) er god, men dens evner inden for matematik, logisk t\u00e6nkning, rumlig t\u00e6nkning og andre evner (orange linje) forringes betydeligt, n\u00e5r opgaven \u00e6ndres en smule.<\/p>\n

De andre modeller viste en lignende nedbrydning med GPT-4 som den bedste.<\/p>\n

P\u00e5 trods af forringelsen var pr\u00e6stationen p\u00e5 kontrafaktiske opgaver stadig bedre end tilf\u00e6ldigt. AI-modellerne fors\u00f8ger at r\u00e6sonnere sig igennem disse opgaver, men er ikke s\u00e6rlig gode til det.<\/p>\n

Resultaterne viser, at AI-modellernes imponerende pr\u00e6stationer i opgaver som collegeeksamener er afh\u00e6ngige af fremragende genkaldelse af tr\u00e6ningsdata, ikke af r\u00e6sonnement. Dette understreger yderligere, at AI-modeller ikke kan generaliseres til usete opgaver,<\/p>\n

Zhaofeng Wu, som er ph.d.-studerende i elektroteknik og datalogi ved MIT, tilknyttet CSAIL og hovedforfatter til artiklen, siger: \"Vi har afd\u00e6kket et fascinerende aspekt af store sprogmodeller: De udm\u00e6rker sig i velkendte scenarier, n\u00e6sten som en veludnyttet sti, men k\u00e6mper, n\u00e5r terr\u00e6net bliver uvant. Denne indsigt er afg\u00f8rende, n\u00e5r vi str\u00e6ber efter at forbedre disse modellers tilpasningsevne og udvide deres anvendelseshorisonter.\"<\/p>\n

Vi s\u00e5 en lignende demonstration af denne manglende evne til at generalisere, da vi unders\u00f8gte, hvor d\u00e5rlige AI-modeller er til at l\u00f8se et forenklet puslespil om at krydse en flod.<\/a><\/p>\n

Forskerne konkluderede, at n\u00e5r udviklere analyserer deres modeller, b\u00f8r de \"betragte abstrakt opgaveevne som l\u00f8srevet fra observeret opgaveudf\u00f8relse.\"<\/p>\n

\"Train-to-test\"-tilgangen kan flytte en model op i benchmarks, men giver ikke et reelt m\u00e5l for, hvordan modellen vil klare sig, n\u00e5r den pr\u00e6senteres for en ny opgave, der skal gennemt\u00e6nkes.<\/p>\n

Forskerne mener, at en del af problemet er, at disse modeller kun er tr\u00e6net p\u00e5 tekst i overfladeform.<\/p>\n

Hvis LLM'er uds\u00e6ttes for mere kontekstualiserede data og semantisk repr\u00e6sentation fra den virkelige verden, kan de m\u00e5ske generalisere, n\u00e5r de pr\u00e6senteres for opgavevariationer.<\/p>","protected":false},"excerpt":{"rendered":"

N\u00e5r ChatGPT giver dig det rigtige svar p\u00e5 din foresp\u00f8rgsel, t\u00e6nker den s\u00e5 igennem foresp\u00f8rgslen eller husker den blot svaret fra sine tr\u00e6ningsdata? Forskere fra MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) har designet en r\u00e6kke tests for at se, om AI-modeller \"t\u00e6nker\" eller bare har en god hukommelse. N\u00e5r du beder en AI-model om at l\u00f8se et matematisk problem som \"Hvad er 27+62?\", kommer den hurtigt tilbage med det rigtige svar: 89. Hvordan kan vi vide, om den forst\u00e5r den underliggende aritmetik eller blot har set problemet i sine tr\u00e6ningsdata? I deres artikel testede 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\/da\/2024\/07\/ai-model-performance-is-it-reasoning-or-simply-reciting\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\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\/da\/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 af\" \/>\n\t<meta name=\"twitter:data1\" content=\"Eugene van der Watt\" \/>\n\t<meta name=\"twitter:label2\" content=\"Estimeret l\u00e6setid\" \/>\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\":\"da-DK\"},{\"@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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