{"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\/pt\/2024\/03\/wmdp-measures-and-reduces-llm-malicious-use-with-unlearning\/","title":{"rendered":"WMDP mede e reduz a utiliza\u00e7\u00e3o maliciosa de LLM com desaprendizagem"},"content":{"rendered":"

Os investigadores lan\u00e7aram um par\u00e2metro de refer\u00eancia para medir se um LLM cont\u00e9m conhecimentos potencialmente perigosos e uma nova t\u00e9cnica para desaprender dados perigosos.<\/strong><\/p>\n

Tem-se debatido muito se os modelos de IA podem ajudar os maus actores a construir uma bomba, planear um ataque \u00e0 ciberseguran\u00e7a<\/a>, ou construir uma arma biol\u00f3gica<\/a>.<\/p>\n

A equipa de investigadores da Scale AI, do Center for AI Safety e de peritos das principais institui\u00e7\u00f5es de ensino, lan\u00e7ou um par\u00e2metro de refer\u00eancia que nos d\u00e1 uma melhor medida do grau de perigosidade de um determinado LLM.<\/p>\n

O indicador de refer\u00eancia Proxy de Armas de Destrui\u00e7\u00e3o Maci\u00e7a (WMDP) \u00e9 um conjunto de dados com 4.157 perguntas de escolha m\u00faltipla sobre conhecimentos perigosos em biosseguran\u00e7a, ciberseguran\u00e7a e seguran\u00e7a qu\u00edmica.<\/p>\n

Quanto mais alta for a pontua\u00e7\u00e3o de um LLM no benchmark, maior \u00e9 o perigo que ele representa ao habilitar potencialmente uma pessoa com inten\u00e7\u00f5es criminosas. Um LLM com uma pontua\u00e7\u00e3o WMDP mais baixa tem menos probabilidades de o ajudar a construir uma bomba ou a criar um novo v\u00edrus.<\/p>\n

A forma tradicional de tornar um LLM mais alinhado \u00e9 recusar pedidos que solicitem dados que possam permitir ac\u00e7\u00f5es maliciosas. Jailbreaking ou afina\u00e7\u00e3o<\/a> um LLM alinhado poderia remover estas protec\u00e7\u00f5es e expor conhecimentos perigosos no conjunto de dados do modelo.<\/p>\n

Se pudermos fazer com que o modelo esque\u00e7a ou desaprenda a informa\u00e7\u00e3o ofensiva, ent\u00e3o n\u00e3o h\u00e1 hip\u00f3tese de ele a fornecer inadvertidamente em resposta a alguma resposta inteligente. desbloqueio<\/a> t\u00e9cnica.<\/p>\n

Em o seu trabalho de investiga\u00e7\u00e3o<\/a>No artigo \"Contrastive Unlearn Tuning\" (CUT), os investigadores explicam como desenvolveram um algoritmo chamado Contrastive Unlearn Tuning (CUT), um m\u00e9todo de afina\u00e7\u00e3o para desaprender conhecimentos perigosos, mantendo a informa\u00e7\u00e3o benigna.<\/p>\n

O m\u00e9todo de afina\u00e7\u00e3o CUT conduz a desaprendizagem da m\u00e1quina atrav\u00e9s da otimiza\u00e7\u00e3o de um \"termo de esquecimento\" para que o modelo se torne menos especialista em assuntos perigosos. Tamb\u00e9m optimiza um \"termo de reten\u00e7\u00e3o\" para que d\u00ea respostas \u00fateis a pedidos benignos.<\/p>\n

A natureza de dupla utiliza\u00e7\u00e3o de muita da informa\u00e7\u00e3o nos conjuntos de dados de treino LLM torna dif\u00edcil desaprender apenas as coisas m\u00e1s, mantendo a informa\u00e7\u00e3o \u00fatil. Utilizando o WMDP, os investigadores conseguiram criar conjuntos de dados \"esquecer\" e \"reter\" para orientar a sua t\u00e9cnica de desaprendizagem CUT.<\/p>\n

Os investigadores utilizaram o WMDP para medir a probabilidade de o modelo ZEPHYR-7B-BETA fornecer informa\u00e7\u00f5es perigosas antes e depois da desaprendizagem utilizando a CUT. Os seus testes centraram-se na bio e na ciberseguran\u00e7a.<\/p>\n

Em seguida, testaram o modelo para ver se o seu desempenho geral tinha sofrido devido ao processo de desaprendizagem.<\/p>\n

\"\"
Precis\u00e3o do WMPD e desempenho do MMLU antes e depois da desaprendizagem do CUT. Fonte: arXiv<\/figcaption><\/figure>\n

Os resultados mostram que o processo de desaprendizagem reduziu significativamente a exatid\u00e3o das respostas a pedidos perigosos, com apenas uma redu\u00e7\u00e3o marginal do desempenho do modelo no par\u00e2metro de refer\u00eancia MMLU.<\/p>\n

Infelizmente, a CUT reduz a precis\u00e3o das respostas em dom\u00ednios estreitamente relacionados, como a virologia introdut\u00f3ria e a seguran\u00e7a inform\u00e1tica. Dar uma resposta \u00fatil a \"Como travar um ciberataque?\" mas n\u00e3o a \"Como efetuar um ciberataque?\" exige mais precis\u00e3o no processo de desaprendizagem.<\/p>\n

Os investigadores descobriram tamb\u00e9m que n\u00e3o podiam eliminar com precis\u00e3o os conhecimentos qu\u00edmicos perigosos, uma vez que estes estavam demasiado interligados com os conhecimentos qu\u00edmicos gerais.<\/p>\n

Utilizando a CUT, os fornecedores de modelos fechados, como o GPT-4, podem desaprender informa\u00e7\u00f5es perigosas, de modo a que, mesmo que sejam sujeitos a afina\u00e7\u00f5es maliciosas ou a jailbreaking, n\u00e3o se lembrem de nenhuma informa\u00e7\u00e3o perigosa para fornecer.<\/p>\n

No entanto, pode fazer o mesmo com modelos de c\u00f3digo aberto, pois o acesso p\u00fablico aos seus pesos significa que podem reaprender dados perigosos se forem treinados com eles.<\/p>\n

Este m\u00e9todo de fazer com que um modelo de IA desaprenda dados perigosos n\u00e3o \u00e9 infal\u00edvel, especialmente para modelos de c\u00f3digo aberto, mas \u00e9 uma adi\u00e7\u00e3o robusta aos actuais alinhamento<\/a> m\u00e9todos.<\/p>","protected":false},"excerpt":{"rendered":"

Os investigadores lan\u00e7aram um par\u00e2metro de refer\u00eancia para medir se um LLM cont\u00e9m conhecimentos potencialmente perigosos e uma nova t\u00e9cnica para desaprender dados perigosos. Tem havido muito debate sobre se os modelos de IA podem ajudar os maus actores a construir uma bomba, planear um ataque \u00e0 ciberseguran\u00e7a ou construir uma arma biol\u00f3gica. A equipa de investigadores da Scale AI, do Center for AI Safety e de especialistas das principais institui\u00e7\u00f5es de ensino, lan\u00e7ou uma refer\u00eancia que nos d\u00e1 uma melhor medida do qu\u00e3o perigoso \u00e9 um determinado LLM. O benchmark Weapons of Mass Destruction Proxy (WMDP) \u00e9 um conjunto de dados de 4.157 perguntas de escolha m\u00faltipla sobre conhecimentos perigosos em biosseguran\u00e7a,<\/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\/pt\/2024\/03\/wmdp-measures-and-reduces-llm-malicious-use-with-unlearning\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\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. There has been much debate over whether AI models could help bad actors build a bomb, plan a cybersecurity attack, or build a bioweapon. The team of researchers from Scale AI, the Center for AI Safety, and experts from leading educational institutions, released a benchmark that gives us a better measure of just how dangerous a particular LLM is. 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