{"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\/nb\/2024\/02\/meta-ucsd-introduce-toolverifier-to-improve-llm-tool-calls\/","title":{"rendered":"Meta og UCSD introduserer ToolVerifier for \u00e5 forbedre LLM-verkt\u00f8yanrop"},"content":{"rendered":"

Forskere fra Meta og University of California San Diego (UCSD) har utviklet ToolVerifier, en metode som forbedrer hvordan LLM-er kaller opp og samhandler med programvareverkt\u00f8y.<\/strong><\/p>\n

For at LLM-er skal bli nyttige som generelle assistenter eller agenter<\/a>m\u00e5 de l\u00e6res opp til \u00e5 bruke ulike verkt\u00f8y eller API-er. Det fungerer \u00e5 finjustere en LLM til \u00e5 bruke et spesifikt verkt\u00f8y, men den virkelige utfordringen er \u00e5 f\u00e5 en LLM til \u00e5 samhandle med nye verkt\u00f8y uten behov for finjusteringer eller f\u00e5 demonstrasjoner.<\/p>\n

N\u00e5r to verkt\u00f8y er sv\u00e6rt like, kan det v\u00e6re spesielt utfordrende for LLM-en \u00e5 velge det riktige verkt\u00f8yet for \u00e5 oppn\u00e5 m\u00e5let. Den n\u00e5v\u00e6rende metoden med \u00e5 gi flere f\u00e5 eksempler for hvert verkt\u00f8y kan ogs\u00e5 ta opp mye av kontekstvinduet som er tilgjengelig for en LLM.<\/p>\n

ToolVerifier er en selvverifiseringsmetode som gj\u00f8r det mulig for LLM \u00e5 stille seg selv sp\u00f8rsm\u00e5l slik at den kan finne ut hvilket verkt\u00f8y som skal brukes og hvilke parametere som skal sendes til verkt\u00f8yet.<\/p>\n

For \u00e5 hjelpe LLM velger ToolVerifier f\u00f8rst ut det best egnede verkt\u00f8yet fra et bibliotek med alternativer, og genererer deretter de riktige parameterne. I hvert av disse trinnene genererer ToolVerifier sp\u00f8rsm\u00e5l for \u00e5 evaluere valgene og skille mellom lignende kandidatverkt\u00f8y.<\/p>\n

Her er et eksempel fra forskningsrapporten som viser prosessen med verkt\u00f8yvalg og parameteravklaring.<\/p>\n

\"\"
ToolVerifier identifiserer f\u00f8rst de to beste verkt\u00f8yene og genererer et verifiseringssp\u00f8rsm\u00e5l. Svaret p\u00e5 sp\u00f8rsm\u00e5let f\u00f8rer til det endelige valget av verkt\u00f8y. En lignende metode brukes til \u00e5 generere parametere. Kilde: arXiv<\/figcaption><\/figure>\n

ToolVerifier ble trent p\u00e5 data best\u00e5ende av en liste med syntetiske verkt\u00f8y, inkludert reise-, bank- og kalenderverkt\u00f8y med tilh\u00f8rende beskrivelser. ToolVerifier ble trent opp til \u00e5 velge riktig verkt\u00f8y utelukkende basert p\u00e5 tittelen og beskrivelsen.<\/p>\n

Etter \u00e5 ha l\u00e6rt opp ToolVerifier i verkt\u00f8yvalg og parameterverifisering, testet forskerne ToolVerifier med fire oppgaver fra ToolBench-referansen som krevde at Llama 2-70B samhandlet med 17 tidligere ukjente verkt\u00f8y.<\/p>\n

Resultatene publisert i avisen<\/a> sier at bruk av ToolVerifier-metoden resulterte i \"en gjennomsnittlig forbedring p\u00e5 22% i forhold til baseline med f\u00e5 bilder, selv i scenarier der forskjellene mellom kandidatverkt\u00f8yene er sv\u00e6rt nyanserte\".<\/p>\n

\"\"
Prosentvis (%) suksessrate for oppgavene Weather, Booking, Home og Cat fra Toolbench-referansen, der modeller med og uten ToolVerifier sammenlignes. Kilde: arXiv<\/figcaption><\/figure>\n

Resultatene viser at ToolVerifier gir en betydelig forbedring av LLMs verkt\u00f8yvalg og n\u00f8yaktig parametergenerering. Metoden ble bare trent og testet for interaksjoner med ett verkt\u00f8y i stedet for flere, men den er likevel lovende.<\/p>\n

Verkt\u00f8yforsterkede LLM-er er en spennende utvikling n\u00e5r det gjelder \u00e5 bruke AI som en generalisert agent. N\u00e5r LLM-ene l\u00e6rer seg \u00e5 bruke flere verkt\u00f8y for \u00e5 n\u00e5 et m\u00e5l, vil de bli enda mer nyttige for oss enn de allerede er.<\/p>\n

Fremtiden der en AI-assistent bestiller en flyreise, koordinerer et m\u00f8te eller gj\u00f8r dagligvareinnkj\u00f8pene for deg, virker ikke veldig langt unna.<\/p>","protected":false},"excerpt":{"rendered":"

Forskere fra Meta og University of California San Diego (UCSD) har utviklet ToolVerifier, en metode som forbedrer hvordan LLM-er kaller opp og samhandler med programvareverkt\u00f8y. For at LLM-er skal bli nyttige som generelle assistenter eller agenter, m\u00e5 de l\u00e6res opp til \u00e5 bruke ulike verkt\u00f8y eller API-er. Det fungerer \u00e5 finjustere en LLM til \u00e5 bruke et spesifikt verkt\u00f8y, men den virkelige utfordringen er \u00e5 f\u00e5 en LLM til \u00e5 samhandle med nye verkt\u00f8y uten behov for finjusteringer eller f\u00e5 demonstrasjoner. N\u00e5r to verkt\u00f8y er sv\u00e6rt like, kan det v\u00e6re spesielt utfordrende for LLM-en \u00e5 velge det riktige verkt\u00f8yet for \u00e5 utf\u00f8re oppgaven.<\/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\/nb\/2024\/02\/meta-ucsd-introduce-toolverifier-to-improve-llm-tool-calls\/\" \/>\n<meta property=\"og:locale\" content=\"nb_NO\" \/>\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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