{"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\/pt\/2023\/06\/new-mit-self-learning-language-models-can-beat-other-llms\/","title":{"rendered":"Os novos modelos lingu\u00edsticos de auto-aprendizagem do MIT podem vencer outros LLM"},"content":{"rendered":"

Nos \u00faltimos seis meses, assistimos a um boom revolucion\u00e1rio de <\/span>Alimentado por IA<\/span><\/a> Os LLM (Large Language Models) ocupam o centro das aten\u00e7\u00f5es. Mas ser\u00e1 sempre necess\u00e1rio que um produto ou servi\u00e7o de IA se baseie em LLMs? De acordo com um documento, os novos modelos lingu\u00edsticos de auto-aprendizagem do MIT n\u00e3o se baseiam em LLMs e podem superar alguns dos outros grandes sistemas de IA que atualmente lideram a ind\u00fastria.<\/span><\/p>\n

Um grupo de investigadores do Laborat\u00f3rio de Ci\u00eancias Inform\u00e1ticas e Intelig\u00eancia Artificial (CSAIL) do MIT desenvolveu uma nova forma de abordar os modelos de linguagem de IA.<\/span><\/p>\n

Trata-se de uma conquista inovadora que real\u00e7a os modelos lingu\u00edsticos mais pequenos e a sua capacidade para resolver problemas de inefici\u00eancia, bem como as preocupa\u00e7\u00f5es com a privacidade que est\u00e3o associadas ao desenvolvimento de grandes modelos de IA baseados em dados textuais.<\/span><\/p>\n

Com o aparecimento de <\/span>O projeto da OpenAI<\/span><\/a> ChatGPT baseado nos modelos lingu\u00edsticos GPT-3 e GPT-4, muitas empresas aderiram \u00e0 corrida da IA, incluindo a Google Bard e outras <\/span>IA generativa<\/span><\/a> sistemas que permitem gerar texto, imagens e at\u00e9 v\u00eddeos.<\/span><\/p>\n

No entanto, para gerar resultados de qualidade impec\u00e1vel, estes sistemas dependem de uma grande quantidade de dados cujo processamento computacional \u00e9 dispendioso. Muitos destes sistemas importam dados para forma\u00e7\u00e3o atrav\u00e9s de API, o que acarreta os seus pr\u00f3prios riscos, como fugas de dados e outros problemas de privacidade.<\/span><\/p>\n

Implica\u00e7\u00e3o textual<\/b><\/h2>\n

De acordo com um novo documento intitulado<\/span> A utiliza\u00e7\u00e3o de um sistema de auto-aprendizagem robusto<\/span><\/a> publicado no reposit\u00f3rio online de pr\u00e9-impress\u00e3o arXiv, os investigadores constatam que os novos modelos lingu\u00edsticos de auto-aprendizagem do MIT podem resolver o problema da compreens\u00e3o de certas tarefas lingu\u00edsticas que os modelos lingu\u00edsticos de grande dimens\u00e3o t\u00eam. Referem-se a esta conquista inovadora como implica\u00e7\u00e3o textual.<\/span><\/p>\n

Os modelos baseiam-se no conceito de que, se houver duas frases - uma premissa e uma hip\u00f3tese - no caso de uma premissa da primeira frase ser verdadeira, \u00e9 prov\u00e1vel que a hip\u00f3tese tamb\u00e9m seja verdadeira.<\/span><\/p>\n

Num<\/span> declara\u00e7\u00e3o<\/span><\/a> publicado no blogue do MIT CSAIL, um exemplo desta estrutura seria que, se \"todos os gatos t\u00eam cauda\", a hip\u00f3tese \"um gato malhado tem cauda\" \u00e9 provavelmente verdadeira. Esta abordagem conduz a um menor enviesamento na <\/span>Modelos de IA<\/span><\/a>o que faz com que os novos modelos lingu\u00edsticos de auto-aprendizagem do MIT superem os modelos lingu\u00edsticos de maior dimens\u00e3o, de acordo com a declara\u00e7\u00e3o.<\/span><\/p>\n

\"Os nossos modelos de implica\u00e7\u00e3o auto-treinados, com 350 milh\u00f5es de par\u00e2metros, sem r\u00f3tulos gerados por humanos, superam os modelos de linguagem supervisionados com 137 a 175 mil milh\u00f5es de par\u00e2metros\", afirmou Hongyin Luo, associado de p\u00f3s-doutoramento do MIT CSAIL, autor principal, num comunicado. \"<\/span><\/p>\n

Acrescentou ainda que esta abordagem poderia ser altamente ben\u00e9fica para os actuais sistemas de IA e remodelar os sistemas de aprendizagem autom\u00e1tica de uma forma mais escal\u00e1vel, fi\u00e1vel e rent\u00e1vel quando se trabalha com modelos lingu\u00edsticos.<\/span><\/p>\n

Os novos modelos lingu\u00edsticos de auto-aprendizagem do MIT s\u00e3o ainda limitados<\/h2>\n

Apesar de os novos modelos lingu\u00edsticos de auto-aprendizagem do MIT prometerem muito no que respeita \u00e0 resolu\u00e7\u00e3o de problemas de classifica\u00e7\u00e3o bin\u00e1ria, ainda est\u00e3o limitados \u00e0 resolu\u00e7\u00e3o de problemas de classifica\u00e7\u00e3o multi-classe. Isto significa que a implica\u00e7\u00e3o textual n\u00e3o funciona t\u00e3o bem quando o modelo \u00e9 apresentado com m\u00faltiplas escolhas.<\/span><\/p>\n

De acordo com James Glass, professor do MIT e investigador principal do CSAIL, que tamb\u00e9m \u00e9 o autor do artigo, esta investiga\u00e7\u00e3o pode lan\u00e7ar luz sobre m\u00e9todos eficientes e eficazes para formar LLMs para compreenderem problemas de implica\u00e7\u00e3o contextual.<\/span><\/p>\n

\"Enquanto o campo dos LLMs est\u00e1 a sofrer mudan\u00e7as r\u00e1pidas e dram\u00e1ticas, esta investiga\u00e7\u00e3o mostra que \u00e9 poss\u00edvel produzir modelos lingu\u00edsticos relativamente compactos que t\u00eam um desempenho muito bom em tarefas de compreens\u00e3o de refer\u00eancia, em compara\u00e7\u00e3o com os seus pares de tamanho aproximadamente igual, ou mesmo modelos lingu\u00edsticos muito maiores, disse ele.<\/span><\/p>\n

Esta investiga\u00e7\u00e3o \u00e9 apenas o in\u00edcio de futuras tecnologias de IA que poder\u00e3o aprender por si pr\u00f3prias e ser mais eficazes, sustent\u00e1veis e centradas na privacidade dos dados. O documento sobre os novos modelos lingu\u00edsticos de auto-aprendizagem do MIT ser\u00e1 apresentado em julho na reuni\u00e3o da Associa\u00e7\u00e3o de Lingu\u00edstica Computacional em Toronto. O projeto conta tamb\u00e9m com o apoio do<\/span> Programa de inova\u00e7\u00e3o em IA de Hong Kong<\/span><\/a>.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

Nos \u00faltimos seis meses, assistimos a um boom revolucion\u00e1rio de LLMs (Large Language Models) alimentados por IA que se tornaram o centro das aten\u00e7\u00f5es. Mas ser\u00e1 sempre necess\u00e1rio que um produto ou servi\u00e7o de IA se baseie em LLMs? De acordo com um artigo, os novos modelos lingu\u00edsticos de auto-aprendizagem do MIT n\u00e3o se baseiam em LLMs e podem superar alguns dos outros grandes sistemas de IA que atualmente lideram a ind\u00fastria. Um grupo de investigadores do Laborat\u00f3rio de Ci\u00eancias Inform\u00e1ticas e Intelig\u00eancia Artificial (CSAIL) do MIT desenvolveu uma nova forma de abordar os modelos lingu\u00edsticos de IA. Trata-se de uma conquista inovadora que d\u00e1 \u00eanfase a modelos lingu\u00edsticos mais pequenos e \u00e0 sua capacidade de abordar<\/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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