{"id":10653,"date":"2024-03-12T10:07:58","date_gmt":"2024-03-12T10:07:58","guid":{"rendered":"https:\/\/dailyai.com\/?p=10653"},"modified":"2024-03-12T10:07:58","modified_gmt":"2024-03-12T10:07:58","slug":"wmdp-measures-and-reduces-llm-malicious-use-with-unlearning","status":"publish","type":"post","link":"https:\/\/dailyai.com\/es\/2024\/03\/wmdp-measures-and-reduces-llm-malicious-use-with-unlearning\/","title":{"rendered":"WMDP mide y reduce el uso malintencionado de LLM con desaprendizaje"},"content":{"rendered":"

Los investigadores dieron a conocer un par\u00e1metro para medir si un LLM contiene conocimientos potencialmente peligrosos y una novedosa t\u00e9cnica para desaprender datos peligrosos.<\/strong><\/p>\n

Se ha debatido mucho sobre si los modelos de IA podr\u00edan ayudar a los malos actores a construir una bomba, planificar un ataque a la ciberseguridad<\/a>o construir un arma biol\u00f3gica<\/a>.<\/p>\n

El equipo de investigadores de Scale AI, el Center for AI Safety y expertos de destacadas instituciones educativas, publicaron un punto de referencia que nos da una mejor medida de lo peligroso que es un determinado LLM.<\/p>\n

El \u00edndice de referencia Weapons of Mass Destruction Proxy (WMDP) es un conjunto de datos de 4.157 preguntas de opci\u00f3n m\u00faltiple sobre conocimientos peligrosos en bioseguridad, ciberseguridad y seguridad qu\u00edmica.<\/p>\n

Cuanto m\u00e1s alta sea la puntuaci\u00f3n de un LLM en el punto de referencia, mayor es el peligro que presenta al permitir potencialmente a una persona con intenciones delictivas. Es menos probable que un LLM con una puntuaci\u00f3n WMDP m\u00e1s baja ayude a construir una bomba o a crear un nuevo virus.<\/p>\n

La forma tradicional de hacer que un LLM est\u00e9 m\u00e1s alineado es rechazar solicitudes que pidan datos que puedan permitir acciones maliciosas. Jailbreaking o puesta a punto<\/a> un LLM alineado podr\u00eda eliminar estas barreras y exponer conocimientos peligrosos en el conjunto de datos del modelo.<\/p>\n

Si puedes hacer que el modelo olvide o desaprenda la informaci\u00f3n ofensiva, entonces no hay posibilidad de que la transmita inadvertidamente en respuesta a alg\u00fan inteligente jailbreaking<\/a> t\u00e9cnica.<\/p>\n

En su trabajo de investigaci\u00f3n<\/a>los investigadores explican c\u00f3mo desarrollaron un algoritmo llamado Contrastive Unlearn Tuning (CUT), un m\u00e9todo de ajuste fino para desaprender conocimientos peligrosos conservando la informaci\u00f3n benigna.<\/p>\n

El m\u00e9todo de ajuste fino CUT lleva a cabo el desaprendizaje autom\u00e1tico optimizando un \"t\u00e9rmino de olvido\" para que el modelo sea menos experto en temas peligrosos. Tambi\u00e9n optimiza un \"t\u00e9rmino de retenci\u00f3n\" para que ofrezca respuestas \u00fatiles a solicitudes benignas.<\/p>\n

La naturaleza de doble uso de gran parte de la informaci\u00f3n de los conjuntos de datos de entrenamiento LLM hace dif\u00edcil desaprender s\u00f3lo lo malo conservando la informaci\u00f3n \u00fatil. Con WMDP, los investigadores pudieron crear conjuntos de datos de \"olvido\" y \"retenci\u00f3n\" para dirigir su t\u00e9cnica de desaprendizaje CUT.<\/p>\n

Los investigadores utilizaron WMDP para medir la probabilidad de que el modelo ZEPHYR-7B-BETA proporcionara informaci\u00f3n peligrosa antes y despu\u00e9s de desaprender utilizando CUT. Las pruebas se centraron en la bioseguridad y la ciberseguridad.<\/p>\n

A continuaci\u00f3n probaron el modelo para ver si su rendimiento general se hab\u00eda resentido debido al proceso de desaprendizaje.<\/p>\n

\"\"
Precisi\u00f3n de WMPD y rendimiento de MMLU antes y despu\u00e9s de desaprender CUT. Fuente: arXiv<\/figcaption><\/figure>\n

Los resultados muestran que el proceso de desaprendizaje redujo significativamente la precisi\u00f3n de las respuestas a las solicitudes peligrosas con s\u00f3lo una reducci\u00f3n marginal del rendimiento del modelo en la prueba de referencia MMLU.<\/p>\n

Por desgracia, la CUT reduce la precisi\u00f3n de las respuestas para campos estrechamente relacionados como la virolog\u00eda introductoria y la seguridad inform\u00e1tica. Dar una respuesta \u00fatil a \"\u00bfC\u00f3mo detener un ciberataque?\" pero no a \"\u00bfC\u00f3mo llevar a cabo un ciberataque?\" requiere m\u00e1s precisi\u00f3n en el proceso de desaprendizaje.<\/p>\n

Los investigadores tambi\u00e9n descubrieron que no pod\u00edan separar con precisi\u00f3n los conocimientos qu\u00edmicos peligrosos, ya que estaban demasiado entrelazados con los conocimientos qu\u00edmicos generales.<\/p>\n

Mediante el uso de CUT, los proveedores de modelos cerrados como GPT-4 podr\u00edan desaprender la informaci\u00f3n peligrosa, de modo que incluso si son sometidos a ajustes maliciosos o jailbreaking, no recuerden ninguna informaci\u00f3n peligrosa que entregar.<\/p>\n

Se podr\u00eda hacer lo mismo con modelos de c\u00f3digo abierto, sin embargo, el acceso p\u00fablico a sus pesos significa que podr\u00edan volver a aprender datos peligrosos si se entrenan con ellos.<\/p>\n

Este m\u00e9todo de hacer que un modelo de IA desaprenda datos peligrosos no es infalible, sobre todo en el caso de los modelos de c\u00f3digo abierto, pero es un complemento s\u00f3lido para los modelos actuales. alineaci\u00f3n<\/a> m\u00e9todos.<\/p>","protected":false},"excerpt":{"rendered":"

Los investigadores publicaron un par\u00e1metro para medir si un LLM contiene conocimientos potencialmente peligrosos y una novedosa t\u00e9cnica para desaprender datos peligrosos. Se ha debatido mucho sobre si los modelos de IA podr\u00edan ayudar a los malos actores a construir una bomba, planificar un ataque de ciberseguridad o fabricar un arma biol\u00f3gica. El equipo de investigadores de Scale AI, el Centro para la Seguridad de la IA y expertos de destacadas instituciones educativas han publicado un punto de referencia que nos da una mejor medida de lo peligroso que es un determinado LLM. El punto de referencia Weapons of Mass Destruction Proxy (WMDP) es un conjunto de datos de 4.157 preguntas de opci\u00f3n m\u00faltiple en torno a conocimientos peligrosos sobre bioseguridad,<\/p>","protected":false},"author":6,"featured_media":10656,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[339,118],"class_list":["post-10653","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-ai-safety","tag-llms"],"yoast_head":"\nWMDP measures and reduces LLM malicious use with unlearning | 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\/03\/wmdp-measures-and-reduces-llm-malicious-use-with-unlearning\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"WMDP measures and reduces LLM malicious use with unlearning | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Researchers released a benchmark to measure whether an LLM contains potentially hazardous knowledge and a novel technique for unlearning dangerous data. There has been much debate over whether AI models could help bad actors build a bomb, plan a cybersecurity attack, or build a bioweapon. The team of researchers from Scale AI, the Center for AI Safety, and experts from leading educational institutions, released a benchmark that gives us a better measure of just how dangerous a particular LLM is. 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