{"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\/sv\/2024\/07\/ai-model-performance-is-it-reasoning-or-simply-reciting\/","title":{"rendered":"AI-modellens prestanda: \u00c4r det ett resonemang eller bara en uppr\u00e4kning?"},"content":{"rendered":"

N\u00e4r ChatGPT ger dig r\u00e4tt svar p\u00e5 din fr\u00e5ga, resonerar den genom f\u00f6rfr\u00e5gan eller kommer den helt enkelt ih\u00e5g svaret fr\u00e5n sina tr\u00e4ningsdata?<\/strong><\/p>\n

Forskare vid MIT:s Computer Science and Artificial Intelligence Laboratory (CSAIL) har utformat en serie tester f\u00f6r att se om AI-modeller \"t\u00e4nker\" eller bara har bra minne.<\/p>\n

N\u00e4r du ber en AI-modell att l\u00f6sa ett matematiskt problem som \"Vad \u00e4r 27+62?\" kommer den snabbt tillbaka med r\u00e4tt svar: 89. Hur kan vi s\u00e4ga om den f\u00f6rst\u00e5r den underliggande aritmetiken eller helt enkelt s\u00e5g problemet i sina tr\u00e4ningsdata?<\/p>\n

I deras papper<\/a>testade forskarna GPT-4, GPT-3.5 Turbo, Claude 1.3 och PaLM2 f\u00f6r att se om de kunde \"generalisera inte bara till osedda instanser av k\u00e4nda uppgifter utan \u00e4ven till nya uppgifter\".<\/p>\n

De utformade en serie med 11 uppgifter som skilde sig n\u00e5got fr\u00e5n de standarduppgifter som LLM-personer i allm\u00e4nhet presterar bra i.<\/p>\n

LLM:erna b\u00f6r prestera lika bra med de \"kontrafaktiska uppgifterna\" om de anv\u00e4nder generella och \u00f6verf\u00f6rbara procedurer f\u00f6r uppgiftsl\u00f6sning.<\/p>\n

Om en LLM \"f\u00f6rst\u00e5r\" matematik b\u00f6r den till exempel ge r\u00e4tt svar p\u00e5 ett matematiskt problem i bas-10 och den s\u00e4llan anv\u00e4nda bas-9.<\/p>\n

H\u00e4r f\u00f6ljer n\u00e5gra exempel p\u00e5 uppgifter och GPT-4:s prestanda.<\/p>\n

\"\"
GPT-4:s prestanda med standarduppgifter (bl\u00e5) och n\u00e5got f\u00f6r\u00e4ndrade kontrafaktiska uppgifter (orange). Exempel p\u00e5 uppgifter och korrekta svar visas h\u00e4r. K\u00e4lla: arXiv<\/figcaption><\/figure>\n

GPT-4:s prestanda i standardtester (bl\u00e5 linje) \u00e4r bra, men dess f\u00f6rm\u00e5ga till matematik, logiska resonemang, spatiala resonemang och andra f\u00f6rm\u00e5gor (orange linje) f\u00f6rs\u00e4mras avsev\u00e4rt n\u00e4r uppgiften \u00e4ndras n\u00e5got.<\/p>\n

De andra modellerna uppvisade liknande nedbrytning med GPT-4 i topp.<\/p>\n

Trots f\u00f6rs\u00e4mringen var resultatet f\u00f6r de kontrafaktiska uppgifterna fortfarande b\u00e4ttre \u00e4n slumpen. AI-modellerna f\u00f6rs\u00f6ker resonera sig fram genom dessa uppgifter men \u00e4r inte s\u00e4rskilt bra p\u00e5 det.<\/p>\n

Resultaten visar att AI-modellernas imponerande prestanda i uppgifter som h\u00f6gskoleprov beror p\u00e5 utm\u00e4rkt \u00e5terkallande av tr\u00e4ningsdata, inte p\u00e5 resonemang. Detta belyser ytterligare att AI-modeller inte kan generaliseras till osynliga uppgifter,<\/p>\n

Zhaofeng Wu, MIT-doktorand i elektroteknik och datavetenskap, CSAIL-ansluten och huvudf\u00f6rfattare till artikeln, s\u00e4ger: \"Vi har uppt\u00e4ckt en fascinerande aspekt av stora spr\u00e5kmodeller: de utm\u00e4rker sig i bekanta scenarier, n\u00e4stan som en v\u00e4l upptrampad stig, men k\u00e4mpar n\u00e4r terr\u00e4ngen blir obekant. Denna insikt \u00e4r avg\u00f6rande n\u00e4r vi str\u00e4var efter att f\u00f6rb\u00e4ttra dessa modellers anpassningsf\u00f6rm\u00e5ga och bredda deras applikationshorisonter.\"<\/p>\n

Vi s\u00e5g en liknande demonstration av denna of\u00f6rm\u00e5ga att generalisera n\u00e4r vi unders\u00f6kte hur d\u00e5liga AI-modeller \u00e4r p\u00e5 att l\u00f6sa ett f\u00f6renklat flod\u00f6verg\u00e5ngspussel.<\/a><\/p>\n

Forskarna drog slutsatsen att n\u00e4r utvecklare analyserar sina modeller b\u00f6r de \"betrakta abstrakt uppgiftsf\u00f6rm\u00e5ga som frist\u00e5ende fr\u00e5n observerad uppgiftsprestation\".<\/p>\n

\"Tr\u00e4na-f\u00f6r-att-testa\"-metoden kan flytta en modell upp\u00e5t i riktm\u00e4rkena, men ger inget verkligt m\u00e5tt p\u00e5 hur modellen kommer att klara sig n\u00e4r den st\u00e4lls inf\u00f6r en ny uppgift att resonera igenom.<\/p>\n

Forskarna menar att en del av problemet \u00e4r att dessa modeller endast tr\u00e4nas p\u00e5 ytformad text.<\/p>\n

Om LLM:er exponeras f\u00f6r mer kontextualiserade data och semantiska representationer fr\u00e5n den verkliga v\u00e4rlden kan de kanske generalisera n\u00e4r de st\u00e4lls inf\u00f6r variationer i uppgifterna.<\/p>","protected":false},"excerpt":{"rendered":"

N\u00e4r ChatGPT ger dig r\u00e4tt svar p\u00e5 din fr\u00e5ga, resonerar den genom f\u00f6rfr\u00e5gan eller kommer den helt enkelt ih\u00e5g svaret fr\u00e5n sina tr\u00e4ningsdata? Forskare vid MIT:s Computer Science and Artificial Intelligence Laboratory (CSAIL) har utformat en serie tester f\u00f6r att se om AI-modeller \"t\u00e4nker\" eller bara har ett bra minne. N\u00e4r du ber en AI-modell att l\u00f6sa ett matematiskt problem som \"Vad \u00e4r 27+62?\" kommer den snabbt tillbaka med r\u00e4tt svar: 89. Hur kan vi s\u00e4ga om den f\u00f6rst\u00e5r den underliggande aritmetiken eller helt enkelt s\u00e5g problemet i sina tr\u00e4ningsdata? I sin artikel testade forskarna 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\/sv\/2024\/07\/ai-model-performance-is-it-reasoning-or-simply-reciting\/\" \/>\n<meta property=\"og:locale\" content=\"sv_SE\" \/>\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\/sv\/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=\"Skriven av\" \/>\n\t<meta name=\"twitter:data1\" content=\"Eugene van der Watt\" \/>\n\t<meta name=\"twitter:label2\" content=\"Ber\u00e4knad l\u00e4stid\" \/>\n\t<meta name=\"twitter:data2\" content=\"3 minuter\" \/>\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\":\"sv-SE\"},{\"@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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