{"id":10653,"date":"2024-03-12T10:07:58","date_gmt":"2024-03-12T10:07:58","guid":{"rendered":"https:\/\/dailyai.com\/?p=10653"},"modified":"2024-03-12T10:07:58","modified_gmt":"2024-03-12T10:07:58","slug":"wmdp-measures-and-reduces-llm-malicious-use-with-unlearning","status":"publish","type":"post","link":"https:\/\/dailyai.com\/it\/2024\/03\/wmdp-measures-and-reduces-llm-malicious-use-with-unlearning\/","title":{"rendered":"WMDP misura e riduce l'uso dannoso di LLM con il disimparare"},"content":{"rendered":"

I ricercatori hanno presentato un parametro per misurare se un LLM contiene conoscenze potenzialmente pericolose e una nuova tecnica per disimparare i dati pericolosi.<\/strong><\/p>\n

Si \u00e8 discusso molto sul fatto che i modelli di IA possano aiutare i malintenzionati a costruire una bomba, a pianificare un'operazione di attacco di cybersicurezza<\/a>, o costruire un'arma biologica<\/a>.<\/p>\n

Il team di ricercatori di Scale AI, del Center for AI Safety e di esperti di importanti istituzioni educative ha rilasciato un benchmark che ci d\u00e0 una misura migliore di quanto sia pericoloso un particolare LLM.<\/p>\n

Il benchmark Weapons of Mass Destruction Proxy (WMDP) \u00e8 un insieme di 4.157 domande a risposta multipla che riguardano conoscenze pericolose in materia di biosicurezza, cybersicurezza e sicurezza chimica.<\/p>\n

Quanto pi\u00f9 alto \u00e8 il punteggio di un LLM nel benchmark, tanto maggiore \u00e8 il pericolo che esso rappresenta in quanto potenzialmente in grado di abilitare una persona con intenti criminali. Un LLM con un punteggio WMDP inferiore ha meno probabilit\u00e0 di aiutare a costruire una bomba o a creare un nuovo virus.<\/p>\n

Il modo tradizionale per rendere un LLM pi\u00f9 allineato consiste nel rifiutare le richieste di dati che potrebbero consentire azioni dannose. Jailbreaking o messa a punto<\/a> un LLM allineato potrebbe rimuovere queste protezioni ed esporre conoscenze pericolose nel set di dati del modello.<\/p>\n

Se si pu\u00f2 far s\u00ec che il modello dimentichi o disimpari l'informazione incriminata, allora non c'\u00e8 alcuna possibilit\u00e0 che la fornisca inavvertitamente in risposta a qualche abile jailbreak<\/a> tecnica.<\/p>\n

In il loro documento di ricerca<\/a>I ricercatori spiegano come hanno sviluppato un algoritmo chiamato Contrastive Unlearn Tuning (CUT), un metodo di regolazione fine per disimparare le conoscenze pericolose mantenendo le informazioni positive.<\/p>\n

Il metodo di messa a punto CUT effettua l'apprendimento automatico ottimizzando un \"termine di dimenticanza\" in modo che il modello diventi meno esperto di argomenti pericolosi. Ottimizza anche un \"termine di mantenimento\", in modo da fornire risposte utili a richieste di tipo benevolo.<\/p>\n

La natura a doppio uso di molte delle informazioni contenute nei dataset di addestramento LLM rende difficile disimparare solo le cose cattive conservando le informazioni utili. Utilizzando WMDP, i ricercatori sono stati in grado di costruire insiemi di dati \"da dimenticare\" e \"da conservare\" per indirizzare la loro tecnica di disimparare CUT.<\/p>\n

I ricercatori hanno utilizzato il WMDP per misurare la probabilit\u00e0 che il modello ZEPHYR-7B-BETA fornisse informazioni pericolose prima e dopo il disapprendimento con CUT. I test si sono concentrati sulla biosicurezza e sulla cybersicurezza.<\/p>\n

Hanno poi testato il modello per verificare se le sue prestazioni generali avessero sofferto a causa del processo di disapprendimento.<\/p>\n

\"\"
Accuratezza del WMPD e prestazioni del MMLU prima e dopo il disimparo del CUT. Fonte: arXiv<\/figcaption><\/figure>\n

I risultati mostrano che il processo di disapprendimento ha ridotto significativamente l'accuratezza delle risposte alle richieste pericolose, con una riduzione solo marginale delle prestazioni del modello sul benchmark MMLU.<\/p>\n

Purtroppo, il CUT riduce l'accuratezza delle risposte in campi strettamente correlati come la virologia introduttiva e la sicurezza informatica. Fornire una risposta utile a \"Come fermare un attacco informatico?\" ma non a \"Come eseguire un attacco informatico?\" richiede una maggiore precisione nel processo di disimparare.<\/p>\n

I ricercatori hanno anche scoperto che non potevano escludere con precisione le conoscenze chimiche pericolose, perch\u00e9 erano troppo strettamente intrecciate con le conoscenze chimiche generali.<\/p>\n

Utilizzando CUT, i fornitori di modelli chiusi come il GPT-4 potrebbero disimparare le informazioni pericolose in modo che, anche se sono sottoposti a una messa a punto o a un jailbreak maligno, non ricordino alcuna informazione pericolosa da fornire.<\/p>\n

Si potrebbe fare lo stesso con i modelli open-source, tuttavia, l'accesso pubblico ai loro pesi significa che potrebbero reimparare dati pericolosi se addestrati su di essi.<\/p>\n

Questo metodo per far s\u00ec che un modello di intelligenza artificiale disimpari i dati pericolosi non \u00e8 infallibile, soprattutto per i modelli open-source, ma \u00e8 un'aggiunta robusta all'attuale allineamento<\/a> metodi.<\/p>","protected":false},"excerpt":{"rendered":"

I ricercatori hanno presentato un parametro per misurare se un LLM contiene conoscenze potenzialmente pericolose e una nuova tecnica per disimparare i dati pericolosi. Si \u00e8 discusso molto sul fatto che i modelli di IA possano aiutare i malintenzionati a costruire una bomba, a pianificare un attacco di cybersicurezza o a costruire un'arma biologica. Il team di ricercatori di Scale AI, del Center for AI Safety e di esperti di importanti istituzioni educative ha rilasciato un benchmark che ci d\u00e0 una misura migliore di quanto sia pericoloso un particolare LLM. Il benchmark Weapons of Mass Destruction Proxy (WMDP) \u00e8 un insieme di 4.157 domande a scelta multipla relative a conoscenze pericolose nel campo della biosicurezza,<\/p>","protected":false},"author":6,"featured_media":10656,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[339,118],"class_list":["post-10653","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-ai-safety","tag-llms"],"yoast_head":"\nWMDP measures and reduces LLM malicious use with unlearning | 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\/03\/wmdp-measures-and-reduces-llm-malicious-use-with-unlearning\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"WMDP measures and reduces LLM malicious use with unlearning | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Researchers released a benchmark to measure whether an LLM contains potentially hazardous knowledge and a novel technique for unlearning dangerous data. 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