{"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\/sv\/2024\/02\/meta-ucsd-introduce-toolverifier-to-improve-llm-tool-calls\/","title":{"rendered":"Meta och UCSD introducerar ToolVerifier f\u00f6r att f\u00f6rb\u00e4ttra LLM-verktygsanrop"},"content":{"rendered":"

Forskare fr\u00e5n Meta och University of California San Diego (UCSD) utvecklade ToolVerifier, en metod som f\u00f6rb\u00e4ttrar hur LLM:er anropar och interagerar med programvaruverktyg.<\/strong><\/p>\n

F\u00f6r att LLM ska bli anv\u00e4ndbara som allm\u00e4nna assistenter eller agenter<\/a>m\u00e5ste de l\u00e4ra sig hur man anv\u00e4nder olika verktyg eller API:er. Att finjustera en LLM f\u00f6r att anv\u00e4nda ett specifikt verktyg fungerar, men den verkliga utmaningen \u00e4r att en LLM ska interagera med nya verktyg utan behov av finjustering eller f\u00e5 demonstrationer.<\/p>\n

N\u00e4r tv\u00e5 verktyg \u00e4r mycket lika kan det vara s\u00e4rskilt utmanande f\u00f6r LLM att v\u00e4lja r\u00e4tt verktyg f\u00f6r att uppn\u00e5 sitt m\u00e5l. Den nuvarande metoden att ge flera exempel med f\u00e5 bilder f\u00f6r varje verktyg kan ocks\u00e5 ta mycket av det kontextf\u00f6nster som \u00e4r tillg\u00e4ngligt f\u00f6r en LLM.<\/p>\n

ToolVerifier \u00e4r en sj\u00e4lvverifieringsmetod som g\u00f6r det m\u00f6jligt f\u00f6r LLM att st\u00e4lla fr\u00e5gor till sig sj\u00e4lv s\u00e5 att den kan r\u00e4kna ut vilket verktyg som ska anv\u00e4ndas och vilka parametrar som ska skickas till verktyget.<\/p>\n

F\u00f6r att hj\u00e4lpa LLM v\u00e4ljer ToolVerifier f\u00f6rst ut det l\u00e4mpligaste verktyget fr\u00e5n ett bibliotek med alternativ och genererar sedan l\u00e4mpliga parametrar. I vart och ett av dessa steg genereras fr\u00e5gor som hj\u00e4lper till att utv\u00e4rdera valet och skilja mellan liknande kandidatverktyg.<\/p>\n

H\u00e4r \u00e4r ett exempel fr\u00e5n forskningsrapporten som visar processen med verktygsval och parameterf\u00f6rtydligande.<\/p>\n

\"\"
ToolVerifier identifierar f\u00f6rst de tv\u00e5 b\u00e4sta verktygen och genererar en verifieringsfr\u00e5ga. Svaret p\u00e5 fr\u00e5gan leder till det slutliga valet av verktyg. En liknande metod anv\u00e4nds f\u00f6r att generera parametrar. K\u00e4lla: arXiv<\/figcaption><\/figure>\n

ToolVerifier tr\u00e4nades p\u00e5 data som bestod av en lista med syntetiska verktyg, bland annat rese-, bank- och kalenderverktyg med tillh\u00f6rande beskrivningar. Den tr\u00e4nades i att v\u00e4lja r\u00e4tt verktyg enbart baserat p\u00e5 titeln och beskrivningen.<\/p>\n

Efter att ha tr\u00e4nats i verktygsval och parameterverifiering testade forskarna ToolVerifier med fyra uppgifter fr\u00e5n ToolBench-riktm\u00e4rket som kr\u00e4vde att Llama 2-70B interagerade med 17 tidigare ok\u00e4nda verktyg.<\/p>\n

Resultaten publicerad i tidningen<\/a> s\u00e4ger att anv\u00e4ndningen av ToolVerifier-metoden resulterade i \"en genomsnittlig f\u00f6rb\u00e4ttring p\u00e5 22% j\u00e4mf\u00f6rt med baslinjer med f\u00e5 bilder, \u00e4ven i scenarier d\u00e4r skillnaderna mellan kandidatverktyg \u00e4r fin nyanserade.\"<\/p>\n

\"\"
Procentuell (%) framg\u00e5ngsgrad f\u00f6r uppgifterna Weather, Booking, Home och Cat fr\u00e5n Toolbench-riktm\u00e4rket som j\u00e4mf\u00f6r modeller med och utan ToolVerifier. K\u00e4lla: arXiv<\/figcaption><\/figure>\n

Resultaten visar att ToolVerifier ger en avsev\u00e4rd f\u00f6rb\u00e4ttring av LLM:s verktygsval och korrekta parametergenerering. Metoden utbildades och testades endast f\u00f6r interaktioner med ett verktyg snarare \u00e4n flera, men det \u00e4r \u00e4nd\u00e5 lovande.<\/p>\n

Verktygsf\u00f6rst\u00e4rkta LLM:er \u00e4r en sp\u00e4nnande utveckling n\u00e4r det g\u00e4ller att anv\u00e4nda AI som en generaliserad agent. N\u00e4r LLM:er l\u00e4r sig att anv\u00e4nda flera verktyg f\u00f6r att uppn\u00e5 ett m\u00e5l kommer de att vara \u00e4nnu mer anv\u00e4ndbara f\u00f6r oss \u00e4n de redan \u00e4r.<\/p>\n

Framtiden d\u00e4r en AI-assistent bokar ett flyg, samordnar ett m\u00f6te eller g\u00f6r dina matink\u00f6p \u00e5t dig verkar inte vara l\u00e5ngt borta.<\/p>","protected":false},"excerpt":{"rendered":"

Forskare fr\u00e5n Meta och University of California San Diego (UCSD) har utvecklat ToolVerifier, en metod som f\u00f6rb\u00e4ttrar hur LLM:er anropar och interagerar med programvaruverktyg. F\u00f6r att LLM:er ska bli anv\u00e4ndbara som allm\u00e4nna assistenter eller agenter m\u00e5ste de l\u00e4ra sig att anv\u00e4nda olika verktyg eller API:er. Att finjustera en LLM f\u00f6r att anv\u00e4nda ett specifikt verktyg fungerar, men den verkliga utmaningen \u00e4r att f\u00e5 en LLM att interagera med nya verktyg utan att beh\u00f6va finjustera eller demonstrera n\u00e5gra f\u00e5 g\u00e5nger. N\u00e4r tv\u00e5 verktyg \u00e4r mycket lika kan det vara s\u00e4rskilt utmanande f\u00f6r LLM att v\u00e4lja r\u00e4tt verktyg f\u00f6r att utf\u00f6ra sitt uppdrag.<\/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\/sv\/2024\/02\/meta-ucsd-introduce-toolverifier-to-improve-llm-tool-calls\/\" \/>\n<meta property=\"og:locale\" content=\"sv_SE\" \/>\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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