{"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\/it\/2024\/04\/deepmind-developed-safe-an-ai-agent-to-fact-check-llms\/","title":{"rendered":"DeepMind ha sviluppato SAFE, un agente di intelligenza artificiale per il controllo dei fatti nei corsi di laurea."},"content":{"rendered":"

I ricercatori di DeepMind e dell'Universit\u00e0 di Stanford hanno sviluppato un agente di IA che controlla i LLM e consente di effettuare un benchmarking della fattualit\u00e0 dei modelli di IA.<\/strong><\/p>\n

Anche i migliori modelli di intelligenza artificiale tendono a allucinare<\/a> a volte. Se chiedete a ChatGPT di fornirvi i fatti su un argomento, pi\u00f9 lunga \u00e8 la sua risposta e pi\u00f9 \u00e8 probabile che includa alcuni fatti non veri.<\/p>\n

Quali modelli sono pi\u00f9 accurati di altri nel generare risposte lunghe? \u00c8 difficile dirlo, perch\u00e9 finora non avevamo un parametro di riferimento che misurasse la fattualit\u00e0 delle risposte lunghe dei LLM.<\/p>\n

DeepMind ha utilizzato il GPT-4 per creare LongFact, un insieme di 2.280 prompt sotto forma di domande relative a 38 argomenti. Questi prompt sollecitano risposte di tipo lungo da parte del LLM sottoposto al test.<\/p>\n

Hanno quindi creato un agente AI che utilizza GPT-3.5-turbo per utilizzare Google e verificare la veridicit\u00e0 delle risposte generate dall'LLM. Il metodo \u00e8 stato chiamato Search-Augmented Factuality Evaluator (SAFE).<\/p>\n

SAFE innanzitutto suddivide la risposta in forma lunga del LLM in singoli fatti. Quindi invia richieste di ricerca a Google Search e valuta la veridicit\u00e0 del fatto in base alle informazioni contenute nei risultati della ricerca.<\/p>\n

Ecco un esempio dal sito carta di ricerca<\/a>.<\/p>\n

\"\"
Una richiesta di ricerca di fatti genera una risposta di forma lunga. La risposta viene suddivisa in singoli fatti, rielaborata in modo da essere autonoma, controllata per la rilevanza e verificata con Google Search. Fonte: arXiv<\/figcaption><\/figure>\n

I ricercatori affermano che SAFE raggiunge \"prestazioni sovrumane\" rispetto agli annotatori umani che effettuano il fact-checking.<\/p>\n

SAFE si \u00e8 trovato d'accordo con il 72% delle annotazioni umane e, nei casi in cui si \u00e8 discostato dagli umani, ha avuto ragione il 76% delle volte. Inoltre, \u00e8 risultato 20 volte pi\u00f9 economico degli annotatori umani in crowdsourcing. Quindi, i LLM sono verificatori di fatti migliori e pi\u00f9 economici degli esseri umani.<\/p>\n

La qualit\u00e0 della risposta dei LLM testati \u00e8 stata misurata in base al numero di fatti nella risposta e al grado di veridicit\u00e0 dei singoli fatti.<\/p>\n

La metrica utilizzata (F1@K) stima il numero \"ideale\" di fatti preferito dall'uomo in una risposta. I test di riferimento hanno utilizzato 64 come mediana per K e 178 come massimo.<\/p>\n

In parole povere, F1@K \u00e8 una misura di \"La risposta mi ha fornito tutti i fatti che volevo?\" combinata con \"Quanti di questi fatti erano veri?\".<\/p>\n

Qual \u00e8 l'LLM pi\u00f9 efficace?<\/h2>\n

I ricercatori hanno utilizzato LongFact per sollecitare 13 LLM delle famiglie Gemini, GPT, Claude e PaLM-2. Hanno poi utilizzato SAFE per valutare la fattualit\u00e0 delle loro risposte.<\/p>\n

Il GPT-4-Turbo \u00e8 in cima alla lista dei modelli pi\u00f9 concreti nella generazione di risposte lunghe. \u00c8 seguito da vicino da Gemini-Ultra e PaLM-2-L-IT-RLHF. I risultati hanno mostrato che gli LLM pi\u00f9 grandi sono pi\u00f9 fattuali di quelli pi\u00f9 piccoli.<\/p>\n

Il calcolo di F1@K probabilmente entusiasmerebbe gli scienziati dei dati, ma, per semplicit\u00e0, questi risultati di benchmark mostrano quanto ogni modello sia efficace quando restituisce risposte di lunghezza media e pi\u00f9 lunghe alle domande.<\/p>\n

\"\"
Prestazioni di fattualit\u00e0 a forma lunga di 13 LLM con K = 64 (il numero mediano di fatti tra tutte le risposte del modello) e K = 178 (il numero massimo di fatti tra tutte le risposte del modello). Fonte: arXiv<\/figcaption><\/figure>\n

SAFE \u00e8 un modo economico ed efficace per quantificare la fattualit\u00e0 dei long-form LLM. \u00c8 pi\u00f9 veloce ed economico degli esseri umani nel fact-checking, ma dipende ancora dalla veridicit\u00e0 delle informazioni che Google restituisce nei risultati della ricerca.<\/p>\n

DeepMind ha rilasciato SAFE per l'uso pubblico e ha suggerito che potrebbe aiutare a migliorare la fattualit\u00e0 dei LLM attraverso un migliore preaddestramento e una messa a punto. Potrebbe anche consentire a un LLM di verificare i fatti prima di presentare l'output a un utente.<\/p>\n

OpenAI sar\u00e0 felice di vedere che una ricerca di Google mostra che GPT-4 batte Gemini in un altro benchmark.<\/p>","protected":false},"excerpt":{"rendered":"

Ricercatori di DeepMind e dell'Universit\u00e0 di Stanford hanno sviluppato un agente di IA che controlla i LLM e consente di effettuare un benchmarking della fattualit\u00e0 dei modelli di IA. Anche i migliori modelli di intelligenza artificiale tendono a volte ad avere allucinazioni. Se si chiede a ChatGPT di fornire i fatti su un argomento, pi\u00f9 lunga \u00e8 la sua risposta, pi\u00f9 \u00e8 probabile che includa alcuni fatti non veri. Quali modelli sono pi\u00f9 accurati di altri quando generano risposte pi\u00f9 lunghe? \u00c8 difficile dirlo, perch\u00e9 finora non avevamo un parametro di riferimento che misurasse la fattualit\u00e0 delle risposte lunghe di LLM. DeepMind ha utilizzato per la prima volta GPT-4 per creare LongFact, una serie di<\/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\/it\/2024\/04\/deepmind-developed-safe-an-ai-agent-to-fact-check-llms\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\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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