{"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\/it\/2024\/02\/meta-ucsd-introduce-toolverifier-to-improve-llm-tool-calls\/","title":{"rendered":"Meta, UCSD introducono ToolVerifier per migliorare le chiamate agli strumenti LLM"},"content":{"rendered":"

I ricercatori di Meta e dell'Universit\u00e0 della California San Diego (UCSD) hanno sviluppato ToolVerifier, un metodo che migliora il modo in cui i LLM chiamano e interagiscono con gli strumenti software.<\/strong><\/p>\n

Per i laureati in Lettere e Filosofia \u00e8 utile diventare assistenti generici o agenti<\/a>\u00c8 necessario insegnare loro come utilizzare vari strumenti o API. La messa a punto di un LLM per l'utilizzo di uno strumento specifico funziona, ma la vera sfida \u00e8 quella di far interagire un LLM con nuovi strumenti senza la necessit\u00e0 di una messa a punto o di poche dimostrazioni.<\/p>\n

Quando due strumenti sono molto simili, pu\u00f2 essere particolarmente difficile per l'LLM scegliere quello giusto per raggiungere il suo obiettivo. L'attuale metodo di fornire diversi esempi di pochi scatti per ogni strumento pu\u00f2 consumare molto della finestra di contesto a disposizione di un LLM.<\/p>\n

ToolVerifier \u00e8 un metodo di autoverifica che consente all'LLM di porsi delle domande per capire quale strumento utilizzare e quali parametri passare allo strumento.<\/p>\n

Per aiutare l'LLM, ToolVerifier seleziona prima lo strumento pi\u00f9 adatto da una libreria di opzioni e poi genera i parametri appropriati. In ognuna di queste fasi, genera domande che aiutano a valutare le sue scelte e a discriminare tra strumenti candidati simili.<\/p>\n

Ecco un esempio tratto dal documento di ricerca che mostra il processo di selezione degli utensili e di chiarificazione dei parametri.<\/p>\n

\"\"
ToolVerifier identifica innanzitutto i due strumenti migliori e genera una domanda di verifica. La risposta alla domanda porta alla scelta finale dello strumento. Un metodo simile viene utilizzato per generare i parametri. Fonte: arXiv<\/figcaption><\/figure>\n

ToolVerifier \u00e8 stato addestrato su dati costituiti da un elenco di strumenti sintetici, tra cui strumenti di viaggio, bancari e di calendario e le relative descrizioni. \u00c8 stato addestrato a selezionare lo strumento appropriato sulla base del titolo e della descrizione.<\/p>\n

Una volta addestrato alla selezione degli strumenti e alla verifica dei parametri, i ricercatori hanno testato ToolVerifier con 4 compiti del benchmark ToolBench che richiedeva a Llama 2-70B di interagire con 17 strumenti mai visti prima.<\/p>\n

I risultati pubblicato sul giornale<\/a> dicono che l'uso del metodo ToolVerifier ha portato a \"un miglioramento medio di 22% rispetto alle linee di base a pochi scatti, anche in scenari in cui le distinzioni tra gli strumenti candidati sono finemente sfumate\".<\/p>\n

\"\"
Percentuale di successo (%) per i task Meteo, Prenotazione, Casa e Gatto del benchmark Toolbench, confrontando i modelli con e senza ToolVerifier. Fonte: arXiv<\/figcaption><\/figure>\n

I risultati mostrano che ToolVerifier offre un miglioramento sostanziale nella selezione degli utensili e nella generazione accurata dei parametri di un LLM. Il metodo \u00e8 stato addestrato e testato solo per interazioni con un singolo utensile piuttosto che con pi\u00f9 utensili, ma \u00e8 comunque promettente.<\/p>\n

Gli LLM arricchiti di strumenti sono uno sviluppo entusiasmante nell'utilizzo dell'intelligenza artificiale come agente generalizzato. Quando i LLM impareranno a utilizzare pi\u00f9 strumenti per raggiungere un obiettivo, saranno ancora pi\u00f9 utili di quanto non lo siano gi\u00e0.<\/p>\n

Il futuro in cui un assistente AI prenoter\u00e0 un volo, coordiner\u00e0 una riunione o far\u00e0 la spesa al posto vostro non sembra molto lontano.<\/p>","protected":false},"excerpt":{"rendered":"

I ricercatori di Meta e dell'Universit\u00e0 della California San Diego (UCSD) hanno sviluppato ToolVerifier, un metodo che migliora il modo in cui i LLM chiamano e interagiscono con gli strumenti software. Affinch\u00e9 gli LLM diventino utili come assistenti o agenti generici, \u00e8 necessario insegnare loro come utilizzare vari strumenti o API. La messa a punto di un LLM per l'uso di uno strumento specifico funziona, ma la vera sfida \u00e8 quella di far interagire un LLM con nuovi strumenti senza la necessit\u00e0 di una messa a punto o di poche dimostrazioni. Quando due strumenti sono molto simili, pu\u00f2 essere particolarmente impegnativo per l'LLM scegliere quello giusto per realizzare il suo scopo.<\/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\/it\/2024\/02\/meta-ucsd-introduce-toolverifier-to-improve-llm-tool-calls\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\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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