{"id":11172,"date":"2024-04-02T09:32:07","date_gmt":"2024-04-02T09:32:07","guid":{"rendered":"https:\/\/dailyai.com\/?p=11172"},"modified":"2024-04-02T09:32:07","modified_gmt":"2024-04-02T09:32:07","slug":"deepmind-developed-safe-an-ai-agent-to-fact-check-llms","status":"publish","type":"post","link":"https:\/\/dailyai.com\/es\/2024\/04\/deepmind-developed-safe-an-ai-agent-to-fact-check-llms\/","title":{"rendered":"DeepMind desarrolla SAFE, un agente de IA para comprobar los hechos de los LLM"},"content":{"rendered":"

Investigadores de DeepMind y la Universidad de Stanford desarrollaron un agente de IA que comprueba los LLM y permite evaluar la factualidad de los modelos de IA.<\/strong><\/p>\n

Incluso los mejores modelos de IA tienden a alucinar<\/a> a veces. Si le pides a ChatGPT que te d\u00e9 los hechos sobre un tema, cuanto m\u00e1s larga sea su respuesta m\u00e1s probable es que incluya algunos hechos que no son ciertos.<\/p>\n

\u00bfQu\u00e9 modelos son m\u00e1s precisos que otros a la hora de generar respuestas largas? Es dif\u00edcil saberlo, porque hasta ahora no dispon\u00edamos de un punto de referencia para medir la veracidad de las respuestas largas de los LLM.<\/p>\n

DeepMind utiliz\u00f3 en primer lugar la GPT-4 para crear LongFact, un conjunto de 2.280 preguntas relacionadas con 38 temas. Estas preguntas obtienen respuestas largas del LLM que se est\u00e1 evaluando.<\/p>\n

A continuaci\u00f3n, crearon un agente de IA con GPT-3.5-turbo para que utilizara Google para verificar la veracidad de las respuestas generadas por el LLM. Llamaron a este m\u00e9todo Search-Augmented Factuality Evaluator (SAFE).<\/p>\n

SAFE descompone primero la respuesta larga del LLM en hechos individuales. A continuaci\u00f3n, env\u00eda solicitudes de b\u00fasqueda a Google Search y razona sobre la veracidad del hecho bas\u00e1ndose en la informaci\u00f3n de los resultados de b\u00fasqueda devueltos.<\/p>\n

He aqu\u00ed un ejemplo de la trabajo de investigaci\u00f3n<\/a>.<\/p>\n

\"\"
Una pregunta de b\u00fasqueda de datos suscita una respuesta extensa. La respuesta se divide en hechos individuales, se revisa para que sea aut\u00f3noma, se comprueba su relevancia y se verifica mediante Google Search. Fuente: arXiv<\/figcaption><\/figure>\n

Los investigadores afirman que SAFE consigue un \"rendimiento sobrehumano\" en comparaci\u00f3n con los anotadores humanos que realizan la comprobaci\u00f3n.<\/p>\n

SAFE coincidi\u00f3 con el 72% de las anotaciones humanas y, en los casos en que discrep\u00f3 de los humanos, acert\u00f3 el 76% de las veces. Adem\u00e1s, resultaba 20 veces m\u00e1s barato que los anotadores humanos. As\u00ed pues, los LLM son mejores y m\u00e1s baratos verificadores de hechos que los humanos.<\/p>\n

La calidad de la respuesta de los LLM evaluados se midi\u00f3 en funci\u00f3n del n\u00famero de factoides en su respuesta combinada con el grado de veracidad de los factoides individuales.<\/p>\n

La m\u00e9trica que utilizaron (F1@K) calcula el n\u00famero \"ideal\" de hechos que prefiere el ser humano en una respuesta. Las pruebas de referencia utilizaron 64 como mediana para K y 178 como m\u00e1ximo.<\/p>\n

En pocas palabras, F1@K es una medida de \"\u00bfMe dio la respuesta tantos datos como quer\u00eda?\" combinada con \"\u00bfCu\u00e1ntos de esos datos eran ciertos?\".<\/p>\n

\u00bfQu\u00e9 LLM es m\u00e1s factual?<\/h2>\n

Los investigadores utilizaron LongFact para preguntar a 13 LLM de las familias Gemini, GPT, Claude y PaLM-2. A continuaci\u00f3n, utilizaron SAFE para evaluar la veracidad de sus respuestas.<\/p>\n

GPT-4-Turbo encabeza la lista como modelo m\u00e1s factual a la hora de generar respuestas largas. Le siguen de cerca Gemini-Ultra y PaLM-2-L-IT-RLHF. Los resultados mostraron que los LLM m\u00e1s grandes son m\u00e1s factuales que los m\u00e1s peque\u00f1os.<\/p>\n

El c\u00e1lculo de F1@K probablemente entusiasmar\u00eda a los cient\u00edficos de datos, pero, en aras de la simplicidad, estos resultados de referencia muestran lo factual que es cada modelo cuando devuelve respuestas de longitud media y m\u00e1s largas a las preguntas.<\/p>\n

\"\"
Resultados de facticidad a largo plazo de 13 LLM con K = 64 (el n\u00famero medio de hechos entre todas las respuestas del modelo) y K = 178 (el n\u00famero m\u00e1ximo de hechos entre todas las respuestas del modelo). Fuente: arXiv<\/figcaption><\/figure>\n

SAFE es una forma barata y eficaz de cuantificar la veracidad de los hechos a largo plazo. Es m\u00e1s r\u00e1pido y barato que la comprobaci\u00f3n de hechos por humanos, pero sigue dependiendo de la veracidad de la informaci\u00f3n que Google devuelve en los resultados de b\u00fasqueda.<\/p>\n

DeepMind lanz\u00f3 SAFE para uso p\u00fablico y sugiri\u00f3 que podr\u00eda ayudar a mejorar la factualidad de los LLM mediante un mejor preentrenamiento y ajuste. Tambi\u00e9n podr\u00eda permitir a un LLM comprobar sus hechos antes de presentar el resultado a un usuario.<\/p>\n

OpenAI se alegrar\u00e1 de ver que un estudio de Google muestra que GPT-4 supera a Gemini en otra prueba comparativa.<\/p>","protected":false},"excerpt":{"rendered":"

Investigadores de DeepMind y la Universidad de Stanford han desarrollado un agente de IA que comprueba los LLM y permite evaluar la factualidad de los modelos de IA. Incluso los mejores modelos de IA tienden a veces a alucinar. Si le pides a ChatGPT que te d\u00e9 los hechos sobre un tema, cuanto m\u00e1s larga sea su respuesta, m\u00e1s probable es que incluya algunos hechos que no son ciertos. \u00bfQu\u00e9 modelos son m\u00e1s precisos que otros a la hora de generar respuestas largas? Es dif\u00edcil saberlo, porque hasta ahora no dispon\u00edamos de un punto de referencia para medir la veracidad de las respuestas largas de LLM. DeepMind utiliz\u00f3 por primera vez GPT-4 para crear LongFact, un conjunto de<\/p>","protected":false},"author":6,"featured_media":11182,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[147,118],"class_list":["post-11172","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-deepmind","tag-llms"],"yoast_head":"\nDeepMind developed SAFE, an AI agent to fact-check LLMs | 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\/es\/2024\/04\/deepmind-developed-safe-an-ai-agent-to-fact-check-llms\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"DeepMind developed SAFE, an AI agent to fact-check LLMs | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Researchers from DeepMind and Stanford University developed an AI agent that fact-checks LLMs and enables benchmarking of AI model factuality. 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