{"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\/fr\/2024\/02\/meta-ucsd-introduce-toolverifier-to-improve-llm-tool-calls\/","title":{"rendered":"Meta et UCSD introduisent ToolVerifier pour am\u00e9liorer les appels d'outils LLM"},"content":{"rendered":"

Des chercheurs de Meta et de l'Universit\u00e9 de Californie \u00e0 San Diego (UCSD) ont mis au point ToolVerifier, une m\u00e9thode qui am\u00e9liore la fa\u00e7on dont les LLM appellent et interagissent avec les outils logiciels.<\/strong><\/p>\n

Pour que les dipl\u00f4m\u00e9s en droit deviennent utiles en tant qu'assistants g\u00e9n\u00e9raux ou en tant qu'experts en droit, il faut qu'ils soient en mesure d'exercer leur m\u00e9tier. agents<\/a>Il faut donc leur apprendre \u00e0 utiliser divers outils ou API. Le r\u00e9glage fin d'un LLM pour utiliser un outil sp\u00e9cifique fonctionne, mais le vrai d\u00e9fi est qu'un LLM interagisse avec de nouveaux outils sans avoir besoin d'un r\u00e9glage fin ou de quelques d\u00e9monstrations.<\/p>\n

Lorsque deux outils sont tr\u00e8s similaires, il peut \u00eatre particuli\u00e8rement difficile pour le LLM de choisir le bon outil pour atteindre son objectif. La m\u00e9thode actuelle, qui consiste \u00e0 fournir plusieurs exemples de courte dur\u00e9e pour chaque outil, peut \u00e9galement consommer une grande partie de la fen\u00eatre contextuelle dont dispose le LLM.<\/p>\n

ToolVerifier est une m\u00e9thode d'auto-v\u00e9rification qui permet au LLM de se poser des questions afin de d\u00e9terminer l'outil \u00e0 utiliser et les param\u00e8tres \u00e0 transmettre \u00e0 l'outil.<\/p>\n

Pour aider le LLM, ToolVerifier s\u00e9lectionne d'abord l'outil le plus appropri\u00e9 \u00e0 partir d'une biblioth\u00e8que d'options et g\u00e9n\u00e8re ensuite les param\u00e8tres appropri\u00e9s. \u00c0 chacune de ces \u00e9tapes, il g\u00e9n\u00e8re des questions qui l'aident \u00e0 \u00e9valuer ses choix et \u00e0 faire la distinction entre des outils candidats similaires.<\/p>\n

Voici un exemple tir\u00e9 du document de recherche montrant le processus de s\u00e9lection des outils et de clarification des param\u00e8tres.<\/p>\n

\"\"
ToolVerifier identifie d'abord les deux meilleurs outils et g\u00e9n\u00e8re une question de v\u00e9rification. La r\u00e9ponse \u00e0 la question conduit au choix final de l'outil. Une m\u00e9thode similaire est utilis\u00e9e pour g\u00e9n\u00e9rer des param\u00e8tres. Source : arXiv<\/figcaption><\/figure>\n

ToolVerifier a \u00e9t\u00e9 entra\u00een\u00e9 sur des donn\u00e9es constitu\u00e9es d'une liste d'outils synth\u00e9tiques comprenant des outils de voyage, de banque et de calendrier et leurs descriptions associ\u00e9es. Il a \u00e9t\u00e9 entra\u00een\u00e9 \u00e0 s\u00e9lectionner l'outil appropri\u00e9 en se basant uniquement sur le titre et la description.<\/p>\n

Une fois form\u00e9s \u00e0 la s\u00e9lection des outils et \u00e0 la v\u00e9rification des param\u00e8tres, les chercheurs ont test\u00e9 ToolVerifier avec 4 t\u00e2ches du benchmark ToolBench qui demandaient au Llama 2-70B d'interagir avec 17 outils in\u00e9dits.<\/p>\n

Les r\u00e9sultats publi\u00e9 dans le journal<\/a> indiquent que l'utilisation de la m\u00e9thode ToolVerifier a entra\u00een\u00e9 \"une am\u00e9lioration moyenne de 22% par rapport aux lignes de base, m\u00eame dans les sc\u00e9narios o\u00f9 les distinctions entre les outils candidats sont finement nuanc\u00e9es\".<\/p>\n

\"\"
Pourcentage (%) de r\u00e9ussite pour les t\u00e2ches Weather, Booking, Home et Cat du benchmark Toolbench en comparant les mod\u00e8les avec et sans ToolVerifier. Source : arXiv<\/figcaption><\/figure>\n

Les r\u00e9sultats montrent que ToolVerifier apporte une am\u00e9lioration substantielle dans la s\u00e9lection des outils d'un LLM et la g\u00e9n\u00e9ration de param\u00e8tres pr\u00e9cis. La m\u00e9thode n'a \u00e9t\u00e9 entra\u00een\u00e9e et test\u00e9e que pour des interactions \u00e0 outil unique et non \u00e0 outils multiples, mais elle est n\u00e9anmoins prometteuse.<\/p>\n

Les LLM augment\u00e9s d'outils constituent un d\u00e9veloppement passionnant dans l'utilisation de l'IA en tant qu'agent g\u00e9n\u00e9ralis\u00e9. Une fois que les LLM auront appris \u00e0 utiliser plusieurs outils pour atteindre un objectif, ils nous seront encore plus utiles qu'ils ne le sont d\u00e9j\u00e0.<\/p>\n

L'avenir o\u00f9 un assistant IA r\u00e9servera un vol, coordonnera une r\u00e9union ou fera vos courses \u00e0 votre place ne semble pas tr\u00e8s \u00e9loign\u00e9.<\/p>","protected":false},"excerpt":{"rendered":"

Des chercheurs de Meta et de l'Universit\u00e9 de Californie \u00e0 San Diego (UCSD) ont mis au point ToolVerifier, une m\u00e9thode qui am\u00e9liore la fa\u00e7on dont les LLM appellent et interagissent avec les outils logiciels. Pour que les LLM deviennent utiles en tant qu'assistants ou agents g\u00e9n\u00e9raux, il faut leur apprendre \u00e0 utiliser divers outils ou API. Le r\u00e9glage fin d'un LLM pour utiliser un outil sp\u00e9cifique fonctionne, mais le vrai d\u00e9fi est pour un LLM d'interagir avec de nouveaux outils sans avoir besoin d'un r\u00e9glage fin ou de quelques d\u00e9monstrations. Lorsque deux outils sont tr\u00e8s similaires, il peut \u00eatre particuli\u00e8rement difficile pour le LLM de choisir le bon outil pour accomplir sa t\u00e2che.<\/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\/fr\/2024\/02\/meta-ucsd-introduce-toolverifier-to-improve-llm-tool-calls\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\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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