{"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\/es\/2023\/10\/chatgpt-shows-strengths-in-emulating-the-peer-review-process\/","title":{"rendered":"ChatGPT muestra sus puntos fuertes al emular el proceso de revisi\u00f3n inter pares"},"content":{"rendered":"

El enfoque convencional de la investigaci\u00f3n cient\u00edfica se basa en gran medida en la revisi\u00f3n por pares, en la que otros cient\u00edficos eval\u00faan y critican meticulosamente un estudio antes de publicarlo.\u00a0<\/b><\/p>\n

Sin embargo, este sistema tradicional se encuentra con cuellos de botella debido al creciente n\u00famero de env\u00edos y a la escasez de revisores humanos disponibles.\u00a0<\/span><\/p>\n

\"Cada vez es m\u00e1s dif\u00edcil para los investigadores obtener comentarios de calidad de los revisores\", afirma James Zou, de la Universidad de Stanford.<\/span><\/p>\n

En respuesta a este reto, Zou y su equipo <\/span>volvi\u00f3 a ChatGPT<\/span><\/a> para descubrir si el chatbot pod\u00eda ofrecer comentarios claros y objetivos sobre art\u00edculos de investigaci\u00f3n. Utilizaron GPT-4 para revisar m\u00e1s de 3000 manuscritos de Nature y m\u00e1s de 1700 art\u00edculos de la Conferencia Internacional sobre Representaciones de Aprendizaje (ICLR).\u00a0<\/span><\/p>\n

Al comparar los comentarios de ChatGPT con los de los revisores humanos sobre los mismos art\u00edculos, descubrieron que m\u00e1s de 50% de los comentarios de la IA sobre los art\u00edculos de Nature y m\u00e1s de 77% sobre los art\u00edculos del ICLR coincid\u00edan con los puntos planteados por los revisores humanos.<\/span><\/p>\n

Ampliando el experimento, el equipo tambi\u00e9n utiliz\u00f3 ChatGPT para evaluar varios cientos de art\u00edculos a\u00fan por revisar en servidores de preimpresi\u00f3n. <\/span><\/p>\n

Al recopilar los comentarios de 308 autores de biolog\u00eda computacional e IA, descubrieron que m\u00e1s de 82% de ellos consideraban que los comentarios de ChatGPT eran, en general, m\u00e1s beneficiosos que algunos de los que hab\u00edan recibido anteriormente de revisores humanos.<\/span><\/p>\n

A pesar de estos prometedores resultados, persisten las dudas sobre la capacidad de la IA para proporcionar informaci\u00f3n matizada y t\u00e9cnicamente detallada.\u00a0<\/span><\/p>\n

Adem\u00e1s, la respuesta de ChatGPT puede ser impredecible, con resultados variables en funci\u00f3n del contenido del estudio.<\/p>\n

Zou reconoce estas limitaciones y se\u00f1ala que algunos investigadores consideraron que los comentarios de ChatGPT eran demasiado vagos. <\/span><\/p>\n

Los investigadores se mostraron optimistas ante la posibilidad de que la GPT-4 pueda ayudar en algunas de las tareas m\u00e1s pesadas del proceso de revisi\u00f3n por pares, se\u00f1alando errores e incoherencias m\u00e1s evidentes.\u00a0<\/span><\/p>\n

M\u00e1s informaci\u00f3n sobre el estudio<\/span><\/h2>\n

ChatGPT -en concreto, el modelo GPT-4- es pr\u00e1cticamente eficaz para revisar estudios cient\u00edficos y proporcionar informaci\u00f3n r\u00e1pida.\u00a0<\/span><\/p>\n

M\u00e1s informaci\u00f3n sobre el estudio:<\/span><\/p>\n

    \n
  1. Objetivo<\/b>: El estudio identifica la dificultad de obtener revisiones por pares humanas de alta calidad. Su objetivo era explorar el uso de grandes modelos ling\u00fc\u00edsticos (LLM) como GPT-4 para proporcionar comentarios cient\u00edficos sobre manuscritos de investigaci\u00f3n.\u00a0<\/span><\/li>\n
  2. Dise\u00f1o de modelos<\/b>: Los investigadores crearon un proceso automatizado utilizando GPT-4 para proporcionar comentarios sobre los PDF completos de art\u00edculos cient\u00edficos. El objetivo es evaluar en qu\u00e9 medida los comentarios generados por LLM pueden complementar o ayudar a los actuales procesos de revisi\u00f3n por pares en la publicaci\u00f3n cient\u00edfica.<\/span><\/li>\n
  3. Resultados<\/b>: La calidad de los comentarios de GPT-4 se evalu\u00f3 mediante dos estudios. El primero consisti\u00f3 en un an\u00e1lisis retrospectivo en el que se compararon los comentarios generados con los comentarios de revisores humanos sobre 3.096 art\u00edculos de 15 revistas de la familia Nature y 1.709 art\u00edculos de la conferencia sobre aprendizaje autom\u00e1tico del ICLR. Se evalu\u00f3 cuantitativamente el solapamiento de los puntos planteados por el GPT-4 y los revisores humanos.<\/span><\/li>\n
  4. El segundo estudio se realiz\u00f3 con 308 investigadores de 110 instituciones estadounidenses de IA y biolog\u00eda computacional. Estos investigadores aportaron sus percepciones sobre la retroalimentaci\u00f3n generada por el sistema GPT-4 en sus propios trabajos.\u00a0<\/span><\/li>\n
  5. Conclusiones<\/b>: Los investigadores encontraron un solapamiento sustancial entre los puntos planteados por el GPT-4 y los revisores humanos, as\u00ed como percepciones positivas de la retroalimentaci\u00f3n generada por el LLM por parte de la mayor\u00eda de los participantes en el estudio de usuarios. Los resultados sugieren que el LLM y la retroalimentaci\u00f3n humana pueden complementarse, aunque tambi\u00e9n se identificaron limitaciones de la retroalimentaci\u00f3n generada por el LLM.<\/span><\/li>\n<\/ol>\n

    Es casi seguro que la GPT-4 fue expuesta a cientos de miles de estudios cient\u00edficos, que<\/span> probablemente contribuya a la capacidad del modelo para diseccionar y criticar con precisi\u00f3n la investigaci\u00f3n de forma similar a los revisores hom\u00f3logos humanos.\u00a0<\/span><\/p>\n

    La IA est\u00e1 cada vez m\u00e1s entrelazada con los procesos acad\u00e9micos. Recientemente, Nature encuest\u00f3 a 1.600 investigadores<\/a> sobre sus opiniones acerca de las IA generativas como ChatGPT, y aunque muchos plantearon su preocupaci\u00f3n por la parcialidad, la mayor\u00eda admiti\u00f3 que su integraci\u00f3n en el proceso cient\u00edfico es inevitable. <\/span><\/p>","protected":false},"excerpt":{"rendered":"

    El enfoque convencional de la investigaci\u00f3n cient\u00edfica depende en gran medida de la revisi\u00f3n por pares, en la que otros cient\u00edficos eval\u00faan y critican meticulosamente un estudio antes de publicarlo. Sin embargo, este sistema tradicional se ve obstaculizado por el creciente n\u00famero de trabajos presentados y la escasez de revisores humanos disponibles. \"A los investigadores les resulta cada vez m\u00e1s dif\u00edcil obtener comentarios de calidad de los revisores\", afirma James Zou, de la Universidad de Stanford. En respuesta a este reto, Zou y su equipo recurrieron a ChatGPT para descubrir si el chatbot pod\u00eda ofrecer opiniones claras y objetivas sobre art\u00edculos de investigaci\u00f3n. Utilizaron GPT-4 para revisar m\u00e1s de 3.000 manuscritos de Nature y otras revistas.<\/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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