{"id":10380,"date":"2024-02-28T10:18:11","date_gmt":"2024-02-28T10:18:11","guid":{"rendered":"https:\/\/dailyai.com\/?p=10380"},"modified":"2024-02-28T10:18:11","modified_gmt":"2024-02-28T10:18:11","slug":"meta-ucsd-introduce-toolverifier-to-improve-llm-tool-calls","status":"publish","type":"post","link":"https:\/\/dailyai.com\/es\/2024\/02\/meta-ucsd-introduce-toolverifier-to-improve-llm-tool-calls\/","title":{"rendered":"Meta y UCSD presentan ToolVerifier para mejorar las llamadas a herramientas LLM"},"content":{"rendered":"

Investigadores de Meta y de la Universidad de California en San Diego (UCSD) desarrollaron ToolVerifier, un m\u00e9todo que mejora la forma en que los LLM llaman a las herramientas de software e interact\u00faan con ellas.<\/strong><\/p>\n

Para que los LLM sean \u00fatiles como asistentes generales o agentes<\/a>Es necesario ense\u00f1arles a utilizar diversas herramientas o API. Ajustar un LLM para que utilice una herramienta espec\u00edfica funciona, pero el verdadero reto es que un LLM interact\u00fae con nuevas herramientas sin necesidad de ajustes finos o demostraciones puntuales.<\/p>\n

Cuando dos herramientas son muy similares, puede ser especialmente dif\u00edcil para el LLM elegir la correcta para lograr su objetivo. El m\u00e9todo actual de proporcionar varios ejemplos de pocas im\u00e1genes para cada herramienta tambi\u00e9n puede consumir gran parte de la ventana de contexto disponible para un LLM.<\/p>\n

ToolVerifier es un m\u00e9todo de autoverificaci\u00f3n que permite al LLM hacerse preguntas a s\u00ed mismo para saber qu\u00e9 herramienta utilizar y qu\u00e9 par\u00e1metros pasar a la herramienta.<\/p>\n

Para ayudar al LLM, ToolVerifier selecciona primero la herramienta m\u00e1s adecuada de una biblioteca de opciones y, a continuaci\u00f3n, genera los par\u00e1metros apropiados. En cada uno de estos pasos, genera preguntas para ayudar a evaluar sus elecciones y discriminar entre herramientas candidatas similares.<\/p>\n

He aqu\u00ed un ejemplo del trabajo de investigaci\u00f3n que muestra el proceso de selecci\u00f3n de herramientas y clarificaci\u00f3n de par\u00e1metros.<\/p>\n

\"\"
ToolVerifier identifica primero las dos mejores herramientas y genera una pregunta de verificaci\u00f3n. La respuesta a la pregunta conduce a la elecci\u00f3n final de la herramienta. Se utiliza un m\u00e9todo similar para generar par\u00e1metros. Fuente: arXiv<\/figcaption><\/figure>\n

ToolVerifier se entren\u00f3 con datos consistentes en una lista de herramientas sint\u00e9ticas que inclu\u00edan herramientas de viajes, bancarias y de calendario y sus descripciones asociadas. Se le entren\u00f3 para seleccionar la herramienta adecuada bas\u00e1ndose \u00fanicamente en el t\u00edtulo y la descripci\u00f3n.<\/p>\n

Una vez entrenado en la selecci\u00f3n de herramientas y la verificaci\u00f3n de par\u00e1metros, los investigadores probaron ToolVerifier con 4 tareas del banco de pruebas ToolBench que requer\u00edan que Llama 2-70B interactuara con 17 herramientas desconocidas hasta entonces.<\/p>\n

Los resultados publicado en el peri\u00f3dico<\/a> afirman que el uso del m\u00e9todo ToolVerifier dio como resultado \"una mejora media de 22% con respecto a las l\u00edneas de base de pocos disparos, incluso en escenarios en los que las distinciones entre las herramientas candidatas son finamente matizadas\".<\/p>\n

\"\"
Porcentaje (%) de \u00e9xito en las tareas Weather, Booking, Home y Cat del banco de pruebas Toolbench comparando modelos con y sin ToolVerifier. Fuente: arXiv<\/figcaption><\/figure>\n

Los resultados muestran que ToolVerifier mejora sustancialmente la selecci\u00f3n de herramientas de un LLM y la generaci\u00f3n de par\u00e1metros precisos. El m\u00e9todo solo se entren\u00f3 y prob\u00f3 para interacciones con una sola herramienta y no con varias, pero no deja de ser prometedor.<\/p>\n

Los LLM mejorados con herramientas son un avance apasionante en el uso de la IA como agente generalizado. Una vez que los LLM aprendan a utilizar varias herramientas para alcanzar un objetivo, nos ser\u00e1n a\u00fan m\u00e1s \u00fatiles de lo que ya son.<\/p>\n

El futuro en el que un asistente de inteligencia artificial reserve un vuelo, coordine una reuni\u00f3n o haga la compra por ti no parece muy lejano.<\/p>","protected":false},"excerpt":{"rendered":"

Investigadores de Meta y la Universidad de California en San Diego (UCSD) han desarrollado ToolVerifier, un m\u00e9todo que mejora la forma en que los LLM llaman a las herramientas de software e interact\u00faan con ellas. Para que los LLM resulten \u00fatiles como asistentes o agentes generales, es necesario ense\u00f1arles a utilizar diversas herramientas o API. Ajustar un LLM para que utilice una herramienta espec\u00edfica funciona, pero el verdadero reto es que un LLM interact\u00fae con nuevas herramientas sin necesidad de ajustes o demostraciones puntuales. Cuando dos herramientas son muy similares, puede ser especialmente dif\u00edcil para el LLM elegir la correcta para lograr sus objetivos.<\/p>","protected":false},"author":6,"featured_media":10390,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[118,131],"class_list":["post-10380","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-llms","tag-meta"],"yoast_head":"\nMeta, UCSD introduce ToolVerifier to improve LLM tool calls | 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\/02\/meta-ucsd-introduce-toolverifier-to-improve-llm-tool-calls\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Meta, UCSD introduce ToolVerifier to improve LLM tool calls | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Researchers from Meta and the University of California San Diego (UCSD) developed ToolVerifier, a method that improves how LLMs call and interact with software tools. 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