{"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\/pt\/2024\/07\/ai-model-performance-is-it-reasoning-or-simply-reciting\/","title":{"rendered":"Desempenho dos modelos de IA: Est\u00e1 a raciocinar ou simplesmente a recitar?"},"content":{"rendered":"

Quando o ChatGPT d\u00e1 a resposta certa ao seu pedido, ele raciocina atrav\u00e9s do pedido ou simplesmente lembra-se da resposta a partir dos seus dados de treino?<\/strong><\/p>\n

Os investigadores do Laborat\u00f3rio de Ci\u00eancias Inform\u00e1ticas e Intelig\u00eancia Artificial (CSAIL) do MIT conceberam uma s\u00e9rie de testes para verificar se os modelos de IA \"pensam\" ou t\u00eam apenas boa mem\u00f3ria.<\/p>\n

Quando se pede a um modelo de IA para resolver um problema de matem\u00e1tica como \"Quanto \u00e9 27+62?\", ele responde rapidamente com a resposta correcta: 89. Como \u00e9 que podemos saber se ele compreende a aritm\u00e9tica subjacente ou se simplesmente viu o problema nos seus dados de treino?<\/p>\n

Em o seu trabalho<\/a>Os investigadores testaram o GPT-4, o GPT-3.5 Turbo, o Claude 1.3 e o PaLM2 para ver se conseguiam \"generalizar n\u00e3o s\u00f3 para inst\u00e2ncias in\u00e9ditas de tarefas conhecidas, mas tamb\u00e9m para novas tarefas\".<\/p>\n

Conceberam uma s\u00e9rie de 11 tarefas que diferiam ligeiramente das tarefas padr\u00e3o em que os LLM t\u00eam geralmente um bom desempenho.<\/p>\n

Os LLM devem ter um desempenho igualmente bom nas \"tarefas contrafactuais\" se utilizarem procedimentos gerais e transfer\u00edveis de resolu\u00e7\u00e3o de tarefas.<\/p>\n

Se um LLM \"entende\" matem\u00e1tica, ent\u00e3o deve dar a resposta correcta a um problema de matem\u00e1tica na base 10 e na raramente usada base 9, por exemplo.<\/p>\n

Eis alguns exemplos das tarefas e do desempenho do GPT-4.<\/p>\n

\"\"
Desempenho do GPT-4 com tarefas padr\u00e3o por defeito (Azul) e tarefas contrafactuais ligeiramente alteradas (Laranja). Exemplos das tarefas e das respostas correctas s\u00e3o mostrados aqui. Fonte: arXiv<\/figcaption><\/figure>\n

O desempenho do GPT-4 em testes padr\u00e3o (linha azul) \u00e9 bom, mas as suas capacidades matem\u00e1ticas, de racioc\u00ednio l\u00f3gico, de racioc\u00ednio espacial e outras (linha laranja) degradam-se significativamente quando a tarefa \u00e9 ligeiramente alterada.<\/p>\n

Os outros modelos registaram uma degrada\u00e7\u00e3o semelhante, com o GPT-4 a ganhar.<\/p>\n

Apesar da degrada\u00e7\u00e3o, o desempenho em tarefas contrafactuais foi ainda melhor do que o acaso. Os modelos de IA tentam raciocinar atrav\u00e9s destas tarefas, mas n\u00e3o s\u00e3o muito bons nisso.<\/p>\n

Os resultados mostram que o desempenho impressionante dos modelos de IA em tarefas como os exames universit\u00e1rios se baseia numa excelente recorda\u00e7\u00e3o dos dados de treino e n\u00e3o no racioc\u00ednio. Este facto real\u00e7a ainda que os modelos de IA n\u00e3o podem ser generalizados a tarefas in\u00e9ditas,<\/p>\n

Zhaofeng Wu, estudante de doutoramento do MIT em engenharia eletrot\u00e9cnica e inform\u00e1tica, afiliado do CSAIL, e principal autor do artigo, afirmou: \"Descobrimos um aspeto fascinante dos modelos de linguagem de grande dimens\u00e3o: s\u00e3o excelentes em cen\u00e1rios familiares, quase como um caminho bem usado, mas t\u00eam dificuldades quando o terreno se torna desconhecido. Este conhecimento \u00e9 crucial, uma vez que nos esfor\u00e7amos por melhorar a adaptabilidade destes modelos e alargar os seus horizontes de aplica\u00e7\u00e3o.\"<\/p>\n

Vimos uma demonstra\u00e7\u00e3o semelhante desta incapacidade de generaliza\u00e7\u00e3o quando explor\u00e1mos como os modelos de IA s\u00e3o maus a resolver um puzzle simplificado de travessia de um rio.<\/a><\/p>\n

Os investigadores conclu\u00edram que, quando os criadores analisam os seus modelos, devem \"considerar a capacidade abstrata da tarefa como separada do desempenho observado da tarefa\".<\/p>\n

A abordagem \"treinar para testar\" pode fazer com que um modelo suba nos par\u00e2metros de refer\u00eancia, mas n\u00e3o oferece uma verdadeira medida de como o modelo se ir\u00e1 comportar quando lhe for apresentada uma nova tarefa para raciocinar.<\/p>\n

Os investigadores sugerem que parte do problema reside no facto de estes modelos serem treinados apenas com texto de forma superficial.<\/p>\n

Se os LLMs forem expostos a mais dados contextualizados do mundo real e \u00e0 representa\u00e7\u00e3o sem\u00e2ntica, poder\u00e3o ser capazes de generalizar quando lhes forem apresentadas varia\u00e7\u00f5es de tarefas.<\/p>","protected":false},"excerpt":{"rendered":"

Quando o ChatGPT lhe d\u00e1 a resposta correcta ao seu pedido, ser\u00e1 que raciocina atrav\u00e9s do pedido ou simplesmente se lembra da resposta a partir dos seus dados de treino? Os investigadores do Laborat\u00f3rio de Ci\u00eancias Inform\u00e1ticas e Intelig\u00eancia Artificial (CSAIL) do MIT conceberam uma s\u00e9rie de testes para verificar se os modelos de IA \"pensam\" ou t\u00eam apenas boa mem\u00f3ria. Quando se pede a um modelo de IA para resolver um problema de matem\u00e1tica como \"Quanto \u00e9 27+62?\", ele responde rapidamente com a resposta correcta: 89. Como \u00e9 que podemos saber se ele compreende a aritm\u00e9tica subjacente ou se simplesmente viu o problema nos seus dados de treino? No seu artigo, os investigadores testaram o 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\/pt\/2024\/07\/ai-model-performance-is-it-reasoning-or-simply-reciting\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\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\/pt\/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=\"Escrito por\" \/>\n\t<meta name=\"twitter:data1\" content=\"Eugene van der Watt\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tempo estimado de leitura\" \/>\n\t<meta name=\"twitter:data2\" content=\"3 minutos\" \/>\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\":\"pt-PT\"},{\"@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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