{"id":6672,"date":"2023-10-22T17:29:32","date_gmt":"2023-10-22T17:29:32","guid":{"rendered":"https:\/\/dailyai.com\/?p=6672"},"modified":"2023-10-22T19:25:00","modified_gmt":"2023-10-22T19:25:00","slug":"chatgpt-shows-strengths-in-emulating-the-peer-review-process","status":"publish","type":"post","link":"https:\/\/dailyai.com\/pt\/2023\/10\/chatgpt-shows-strengths-in-emulating-the-peer-review-process\/","title":{"rendered":"ChatGPT mostra pontos fortes na emula\u00e7\u00e3o do processo de revis\u00e3o por pares"},"content":{"rendered":"

A abordagem convencional \u00e0 investiga\u00e7\u00e3o cient\u00edfica baseia-se fortemente na revis\u00e3o por pares, em que outros cientistas avaliam e criticam meticulosamente um estudo antes de este ser publicado.\u00a0<\/b><\/p>\n

No entanto, este sistema tradicional est\u00e1 a sofrer um estrangulamento devido ao n\u00famero crescente de submiss\u00f5es e \u00e0 escassez de revisores humanos dispon\u00edveis.\u00a0<\/span><\/p>\n

\"Est\u00e1 a tornar-se cada vez mais dif\u00edcil para os investigadores obterem feedback de alta qualidade dos revisores\", afirma James Zou, da Universidade de Stanford.<\/span><\/p>\n

Em resposta a este desafio, Zou e a sua equipa <\/span>virou-se para o ChatGPT<\/span><\/a> para descobrir se o chatbot podia dar um feedback claro e objetivo sobre artigos de investiga\u00e7\u00e3o. Utilizaram o GPT-4 para analisar mais de 3000 manuscritos da Nature e mais de 1700 artigos da Confer\u00eancia Internacional sobre Representa\u00e7\u00f5es de Aprendizagem (ICLR).\u00a0<\/span><\/p>\n

Ao comparar o feedback do ChatGPT com o dos revisores humanos sobre os mesmos artigos, verificaram que mais de 50% dos coment\u00e1rios da IA sobre os artigos da Nature e mais de 77% sobre os artigos do ICLR estavam em sintonia com os pontos levantados pelos revisores humanos.<\/span><\/p>\n

Alargando a experi\u00eancia, a equipa tamb\u00e9m utilizou o ChatGPT para avaliar v\u00e1rias centenas de artigos ainda n\u00e3o revistos por pares em servidores de pr\u00e9-impress\u00e3o. <\/span><\/p>\n

Recolhendo o feedback de 308 autores em IA e biologia computacional, descobriram que mais de 82% deles consideraram o feedback do ChatGPT geralmente mais ben\u00e9fico do que algum do feedback anterior que receberam de revisores humanos.<\/span><\/p>\n

Apesar destes resultados promissores, persistem as preocupa\u00e7\u00f5es quanto \u00e0 capacidade da IA para fornecer um feedback matizado e tecnicamente pormenorizado.\u00a0<\/span><\/p>\n

Al\u00e9m disso, o feedback do ChatGPT pode ser imprevis\u00edvel, com resultados vari\u00e1veis consoante o conte\u00fado do estudo.<\/p>\n

Zou reconhece estas limita\u00e7\u00f5es, referindo que alguns investigadores consideraram o feedback do ChatGPT demasiado vago. <\/span><\/p>\n

Os investigadores continuam optimistas quanto ao facto de a GPT-4 poder ajudar em parte o trabalho pesado do processo de revis\u00e3o por pares, assinalando erros e inconsist\u00eancias mais \u00f3bvios.\u00a0<\/span><\/p>\n

Mais informa\u00e7\u00f5es sobre o estudo<\/span><\/h2>\n

O ChatGPT - especificamente, o modelo GPT-4 - \u00e9 praticamente eficaz na revis\u00e3o de estudos cient\u00edficos e no fornecimento de feedback r\u00e1pido.\u00a0<\/span><\/p>\n

Mais informa\u00e7\u00f5es sobre o estudo:<\/span><\/p>\n

    \n
  1. Objetivo<\/b>: O estudo identifica a dificuldade em obter revis\u00f5es humanas de alta qualidade pelos pares. O seu objetivo era explorar a utiliza\u00e7\u00e3o de modelos de linguagem de grande dimens\u00e3o (LLM), como o GPT-4, para fornecer feedback cient\u00edfico sobre manuscritos de investiga\u00e7\u00e3o.\u00a0<\/span><\/li>\n
  2. Conce\u00e7\u00e3o do modelo<\/b>: Os investigadores criaram um processo automatizado utilizando o GPT-4 para fornecer coment\u00e1rios sobre os PDFs completos de artigos cient\u00edficos. Este processo foi concebido para avaliar at\u00e9 que ponto o feedback gerado pelo LLM pode complementar ou ajudar os processos de revis\u00e3o por pares existentes na publica\u00e7\u00e3o cient\u00edfica.<\/span><\/li>\n
  3. Resultados<\/b>: A qualidade do feedback do GPT-4 foi avaliada atrav\u00e9s de dois estudos. O primeiro envolveu uma an\u00e1lise retrospetiva, em que o feedback gerado foi comparado com o feedback de revisores humanos sobre 3.096 artigos de 15 revistas da fam\u00edlia Nature e 1.709 artigos da confer\u00eancia de aprendizagem autom\u00e1tica ICLR. A sobreposi\u00e7\u00e3o dos pontos levantados pelo GPT-4 e pelos revisores humanos foi avaliada quantitativamente.<\/span><\/li>\n
  4. O segundo estudo foi realizado com 308 investigadores de 110 institui\u00e7\u00f5es norte-americanas no dom\u00ednio da IA e da biologia computacional. Estes investigadores forneceram as suas percep\u00e7\u00f5es sobre o feedback gerado pelo sistema GPT-4 nos seus pr\u00f3prios artigos.\u00a0<\/span><\/li>\n
  5. Conclus\u00f5es<\/b>: Os investigadores encontraram uma sobreposi\u00e7\u00e3o substancial entre os pontos levantados pelo GPT-4 e pelos revisores humanos e percep\u00e7\u00f5es positivas do feedback gerado pela MLM por parte da maioria dos participantes no estudo de utilizadores. Os resultados sugerem que o LLM e o feedback humano podem complementar-se mutuamente, embora tamb\u00e9m tenham sido identificadas limita\u00e7\u00f5es do feedback gerado pelo LLM.<\/span><\/li>\n<\/ol>\n

    A GPT-4 foi quase de certeza exposta a centenas de milhares de estudos cient\u00edficos, que<\/span> contribui provavelmente para a capacidade do modelo de dissecar e criticar com exatid\u00e3o a investiga\u00e7\u00e3o, \u00e0 semelhan\u00e7a dos revisores humanos.\u00a0<\/span><\/p>\n

    A IA est\u00e1 cada vez mais ligada aos processos acad\u00e9micos. Recentemente, a Nature inquiriu 1.600 investigadores<\/a> sobre as suas opini\u00f5es relativamente \u00e0s IA generativas como o ChatGPT e, embora muitos tenham manifestado preocupa\u00e7\u00e3o com a parcialidade, a maioria admitiu que a sua integra\u00e7\u00e3o no processo cient\u00edfico \u00e9 inevit\u00e1vel. <\/span><\/p>","protected":false},"excerpt":{"rendered":"

    A abordagem convencional da investiga\u00e7\u00e3o cient\u00edfica assenta fortemente na revis\u00e3o por pares, em que outros cientistas avaliam e criticam meticulosamente um estudo antes de este ser publicado. No entanto, este sistema tradicional est\u00e1 a sofrer um estrangulamento devido ao n\u00famero crescente de submiss\u00f5es e \u00e0 escassez de revisores humanos dispon\u00edveis. \"Est\u00e1 a tornar-se cada vez mais dif\u00edcil para os investigadores obterem feedback de alta qualidade dos revisores\", afirma James Zou, da Universidade de Stanford. Em resposta a este desafio, Zou e a sua equipa recorreram ao ChatGPT para descobrir se o chatbot poderia fornecer um feedback claro e objetivo sobre os artigos de investiga\u00e7\u00e3o. Utilizaram o GPT-4 para analisar mais de 3.000 manuscritos da Nature e mais<\/p>","protected":false},"author":2,"featured_media":6673,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[88],"tags":[115,143,93,135],"class_list":["post-6672","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ethics","tag-chatgpt","tag-natural-language-processing","tag-openai","tag-science"],"yoast_head":"\nChatGPT shows strengths in emulating the peer review process | DailyAI<\/title>\n<meta name=\"description\" content=\"The conventional approach to scientific research heavily relies on peer review, where other scientists meticulously evaluate and critique a study before it gets published.\u00a0\" \/>\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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