{"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\/da\/2024\/02\/meta-ucsd-introduce-toolverifier-to-improve-llm-tool-calls\/","title":{"rendered":"Meta og UCSD introducerer ToolVerifier for at forbedre LLM-v\u00e6rkt\u00f8jskald"},"content":{"rendered":"

Forskere fra Meta og University of California San Diego (UCSD) har udviklet ToolVerifier, en metode, der forbedrer, hvordan LLM'er kalder og interagerer med softwarev\u00e6rkt\u00f8jer.<\/strong><\/p>\n

For at LLM'er kan blive nyttige som generelle assistenter eller agenter<\/a>De skal undervises i, hvordan man bruger forskellige v\u00e6rkt\u00f8jer eller API'er. Det fungerer at finjustere en LLM til at bruge et bestemt v\u00e6rkt\u00f8j, men den virkelige udfordring er at f\u00e5 en LLM til at interagere med nye v\u00e6rkt\u00f8jer uden behov for finjustering eller f\u00e5 demonstrationer.<\/p>\n

N\u00e5r to v\u00e6rkt\u00f8jer er meget ens, kan det v\u00e6re s\u00e6rligt udfordrende for LLM'en at v\u00e6lge det rigtige til at n\u00e5 sit m\u00e5l. Den nuv\u00e6rende metode med at give flere f\u00e5 eksempler for hvert v\u00e6rkt\u00f8j kan ogs\u00e5 optage en stor del af det kontekstvindue, der er tilg\u00e6ngeligt for en LLM.<\/p>\n

ToolVerifier er en selvverificeringsmetode, der g\u00f8r det muligt for LLM at stille sig selv sp\u00f8rgsm\u00e5l, s\u00e5 den kan finde ud af, hvilket v\u00e6rkt\u00f8j der skal bruges, og hvilke parametre der skal sendes til v\u00e6rkt\u00f8jet.<\/p>\n

For at hj\u00e6lpe LLM v\u00e6lger ToolVerifier f\u00f8rst det bedst egnede v\u00e6rkt\u00f8j fra et bibliotek af muligheder og genererer derefter de relevante parametre. Ved hvert af disse trin genererer den sp\u00f8rgsm\u00e5l, der hj\u00e6lper med at evaluere valgene og skelne mellem lignende kandidatv\u00e6rkt\u00f8jer.<\/p>\n

Her er et eksempel fra forskningsartiklen, der viser processen med v\u00e6rkt\u00f8jsvalg og parameterafklaring.<\/p>\n

\"\"
ToolVerifier identificerer f\u00f8rst de to bedste v\u00e6rkt\u00f8jer og genererer et verifikationssp\u00f8rgsm\u00e5l. Svaret p\u00e5 sp\u00f8rgsm\u00e5let f\u00f8rer til det endelige valg af v\u00e6rkt\u00f8j. En lignende metode bruges til at generere parametre. Kilde: arXiv<\/figcaption><\/figure>\n

ToolVerifier blev tr\u00e6net p\u00e5 data, der bestod af en liste over syntetiske v\u00e6rkt\u00f8jer, herunder rejse-, bank- og kalenderv\u00e6rkt\u00f8jer og deres tilh\u00f8rende beskrivelser. Den blev tr\u00e6net til at v\u00e6lge det rette v\u00e6rkt\u00f8j udelukkende ud fra titlen og beskrivelsen.<\/p>\n

Efter at have tr\u00e6net v\u00e6rkt\u00f8jsvalg og parameterverifikation testede forskerne ToolVerifier med 4 opgaver fra ToolBench-benchmarket, som kr\u00e6vede, at Llama 2-70B interagerede med 17 tidligere usete v\u00e6rkt\u00f8jer.<\/p>\n

Resultaterne offentliggjort i avisen<\/a> siger, at brugen af ToolVerifier-metoden resulterede i \"en gennemsnitlig forbedring p\u00e5 22% i forhold til baselines med f\u00e5 skud, selv i scenarier, hvor forskellene mellem kandidatv\u00e6rkt\u00f8jer er fint nuancerede.\"<\/p>\n

\"\"
Procentvis (%) succesrate for opgaverne Weather, Booking, Home og Cat fra Toolbench-benchmarkingen, der sammenligner modeller med og uden ToolVerifier. Kilde: arXiv<\/figcaption><\/figure>\n

Resultaterne viser, at ToolVerifier giver en betydelig forbedring af en LLM's v\u00e6rkt\u00f8jsvalg og n\u00f8jagtige parametergenerering. Metoden blev kun tr\u00e6net og testet for interaktioner med et enkelt v\u00e6rkt\u00f8j i stedet for flere, men det er ikke desto mindre lovende.<\/p>\n

V\u00e6rkt\u00f8jsudvidede LLM'er er en sp\u00e6ndende udvikling i brugen af AI som en generaliseret agent. N\u00e5r LLM'er l\u00e6rer at bruge flere v\u00e6rkt\u00f8jer til at n\u00e5 et m\u00e5l, vil de v\u00e6re endnu mere nyttige for os, end de allerede er.<\/p>\n

Fremtiden, hvor en AI-assistent booker et fly, koordinerer et m\u00f8de eller k\u00f8ber ind for dig, synes ikke at v\u00e6re langt v\u00e6k.<\/p>","protected":false},"excerpt":{"rendered":"

Forskere fra Meta og University of California San Diego (UCSD) har udviklet ToolVerifier, en metode, der forbedrer, hvordan LLM'er kalder og interagerer med softwarev\u00e6rkt\u00f8jer. For at LLM'er kan blive nyttige som generelle assistenter eller agenter, skal de l\u00e6re at bruge forskellige v\u00e6rkt\u00f8jer eller API'er. Det fungerer at finjustere en LLM til at bruge et bestemt v\u00e6rkt\u00f8j, men den virkelige udfordring er, at en LLM skal kunne interagere med nye v\u00e6rkt\u00f8jer uden behov for finjustering eller f\u00e5 demonstrationer. N\u00e5r to v\u00e6rkt\u00f8jer er meget ens, kan det v\u00e6re s\u00e6rligt udfordrende for LLM'en at v\u00e6lge det rigtige til at udf\u00f8re sin opgave.<\/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\/da\/2024\/02\/meta-ucsd-introduce-toolverifier-to-improve-llm-tool-calls\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\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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