{"id":11172,"date":"2024-04-02T09:32:07","date_gmt":"2024-04-02T09:32:07","guid":{"rendered":"https:\/\/dailyai.com\/?p=11172"},"modified":"2024-04-02T09:32:07","modified_gmt":"2024-04-02T09:32:07","slug":"deepmind-developed-safe-an-ai-agent-to-fact-check-llms","status":"publish","type":"post","link":"https:\/\/dailyai.com\/nb\/2024\/04\/deepmind-developed-safe-an-ai-agent-to-fact-check-llms\/","title":{"rendered":"DeepMind har utviklet SAFE, en AI-agent for \u00e5 faktasjekke LLM-er"},"content":{"rendered":"

Forskere fra DeepMind og Stanford University har utviklet en AI-agent som faktasjekker LLM-er og muliggj\u00f8r benchmarking av AI-modellers faktisitet.<\/strong><\/p>\n

Selv de beste AI-modellene har fortsatt en tendens til \u00e5 hallusinere<\/a> til tider. Hvis du ber ChatGPT om \u00e5 gi deg fakta om et emne, er det mer sannsynlig at svaret inneholder fakta som ikke er sanne, jo lengre det er.<\/p>\n

Hvilke modeller er mer faktabaserte enn andre n\u00e5r de genererer lengre svar? Det er vanskelig \u00e5 si, for frem til n\u00e5 har vi ikke hatt noen m\u00e5lestokk for hvor faktabaserte de lange LLM-svarene er.<\/p>\n

DeepMind brukte f\u00f8rst GPT-4 til \u00e5 lage LongFact, et sett med 2280 sp\u00f8rsm\u00e5l i form av sp\u00f8rsm\u00e5l knyttet til 38 emner. Disse sp\u00f8rsm\u00e5lene fremkaller lange svar fra LLM-en som testes.<\/p>\n

Deretter skapte de en AI-agent ved hjelp av GPT-3.5-turbo for \u00e5 bruke Google til \u00e5 verifisere hvor faktabaserte svarene som LLM genererte, var. De kalte metoden Search-Augmented Factuality Evaluator (SAFE).<\/p>\n

SAFE bryter f\u00f8rst opp det lange svaret fra LLM i individuelle fakta. Deretter sender den s\u00f8keforesp\u00f8rsler til Google S\u00f8k og tar stilling til sannhetsgehalten i faktaene basert p\u00e5 informasjonen i s\u00f8keresultatene som returneres.<\/p>\n

Her er et eksempel fra forskningsoppgave<\/a>.<\/p>\n

\"\"
En faktas\u00f8kende ledetekst fremkaller et langt svar. Svaret deles opp i individuelle fakta, revideres for \u00e5 v\u00e6re selvstendig, sjekkes for relevans og sjekkes ved hjelp av Google Search. Kilde: arXiv<\/figcaption><\/figure>\n

Forskerne sier at SAFE oppn\u00e5r \"overmenneskelig ytelse\" sammenlignet med menneskelige kommentatorer som utf\u00f8rer faktasjekken.<\/p>\n

SAFE var enig med 72% av de menneskelige annotasjonene, og der den var uenig med de menneskelige annotasjonene, hadde den rett i 76% av tilfellene. SAFE var ogs\u00e5 20 ganger billigere enn menneskelige kommentatorer. LLM-er er alts\u00e5 bedre og billigere faktasjekkere enn mennesker.<\/p>\n

Kvaliteten p\u00e5 svaret fra de testede LLM-ene ble m\u00e5lt ut fra antall faktoider i svaret kombinert med hvor faktabaserte de enkelte faktoidene var.<\/p>\n

M\u00e5lingen de brukte (F1@K), estimerer det \"ideelle\" antallet fakta i et svar. I referansetestene ble 64 brukt som median for K og 178 som maksimum.<\/p>\n

Enkelt sagt er F1@K et m\u00e5l p\u00e5 \"Ga svaret meg s\u00e5 mange fakta som jeg \u00f8nsket?\" kombinert med \"Hvor mange av disse faktaene var sanne?\".<\/p>\n

Hvilken LLM er mest saklig?<\/h2>\n

Forskerne brukte LongFact til \u00e5 sp\u00f8rre 13 LLM-er fra Gemini-, GPT-, Claude- og PaLM-2-familiene. Deretter brukte de SAFE til \u00e5 evaluere hvor faktabaserte svarene deres var.<\/p>\n

GPT-4-Turbo topper listen som den mest faktabaserte modellen n\u00e5r det gjelder \u00e5 generere lange svar. Den ble tett fulgt av Gemini-Ultra og PaLM-2-L-IT-RLHF. Resultatene viste at st\u00f8rre LLM-er er mer faktabaserte enn mindre LLM-er.<\/p>\n

F1@K-beregningen ville sannsynligvis begeistret dataforskere, men for enkelhets skyld viser disse referanseresultatene hvor faktabasert hver modell er n\u00e5r den returnerer gjennomsnittlig lengde og lengre svar p\u00e5 sp\u00f8rsm\u00e5lene.<\/p>\n

\"\"
Langformfaktualitet for 13 LLM-er med K = 64 (medianantallet av fakta blant alle modellsvar) og K = 178 (det maksimale antallet fakta blant alle modellsvar). Kilde: arXiv<\/figcaption><\/figure>\n

SAFE er en billig og effektiv m\u00e5te \u00e5 kvantifisere LLM-langformfakta p\u00e5. Det er raskere og billigere enn menneskelig faktasjekk, men det avhenger fortsatt av sannhetsgehalten i informasjonen som Google returnerer i s\u00f8keresultatene.<\/p>\n

DeepMind lanserte SAFE for offentlig bruk og antydet at det kunne bidra til \u00e5 forbedre LLM-faktualiteten ved hjelp av bedre forh\u00e5ndstrening og finjustering. Det kan ogs\u00e5 gj\u00f8re det mulig for en LLM \u00e5 sjekke fakta f\u00f8r den presenterer resultatet for en bruker.<\/p>\n

OpenAI vil bli glade for \u00e5 se at forskning fra Google viser at GPT-4 sl\u00e5r Gemini i enda en benchmark.<\/p>","protected":false},"excerpt":{"rendered":"

Forskere fra DeepMind og Stanford University har utviklet en AI-agent som faktasjekker LLM-er og muliggj\u00f8r benchmarking av AI-modellers faktakvalitet. Selv de beste AI-modellene har en tendens til \u00e5 hallusinere av og til. Hvis du ber ChatGPT om \u00e5 gi deg fakta om et emne, er det mer sannsynlig at svaret inneholder noen fakta som ikke er sanne, jo lengre det er. Hvilke modeller er mer faktabaserte enn andre n\u00e5r de genererer lengre svar? Det er vanskelig \u00e5 si, for frem til n\u00e5 har vi ikke hatt noen m\u00e5lestokk for hvor faktabaserte LLM-svarene er. DeepMind brukte f\u00f8rst GPT-4 til \u00e5 lage LongFact, et sett med<\/p>","protected":false},"author":6,"featured_media":11182,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[147,118],"class_list":["post-11172","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-deepmind","tag-llms"],"yoast_head":"\nDeepMind developed SAFE, an AI agent to fact-check LLMs | 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\/04\/deepmind-developed-safe-an-ai-agent-to-fact-check-llms\/\" \/>\n<meta property=\"og:locale\" content=\"nb_NO\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"DeepMind developed SAFE, an AI agent to fact-check LLMs | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Researchers from DeepMind and Stanford University developed an AI agent that fact-checks LLMs and enables benchmarking of AI model factuality. 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