{"id":6384,"date":"2023-10-12T14:57:12","date_gmt":"2023-10-12T14:57:12","guid":{"rendered":"https:\/\/dailyai.com\/?p=6384"},"modified":"2023-10-13T10:04:42","modified_gmt":"2023-10-13T10:04:42","slug":"simply-fine-tuning-llms-can-remove-alignment-guardrails","status":"publish","type":"post","link":"https:\/\/dailyai.com\/fr\/2023\/10\/simply-fine-tuning-llms-can-remove-alignment-guardrails\/","title":{"rendered":"Un simple r\u00e9glage fin des LLM permet de supprimer les garde-fous en mati\u00e8re d'alignement"},"content":{"rendered":"

Les LLM commerciaux tels que GPT-3.5 et OpenAI disposent de garde-fous pour s'assurer que les mod\u00e8les sont align\u00e9s et ne g\u00e9n\u00e8rent pas de r\u00e9ponses dangereuses. Un simple r\u00e9glage fin du mod\u00e8le pourrait contourner ces mesures de s\u00e9curit\u00e9.<\/strong><\/p>\n

Pour qu'un LLM g\u00e9n\u00e9ral soit vraiment utile dans un but sp\u00e9cifique, il doit \u00eatre affin\u00e9 sur un ensemble plus restreint de donn\u00e9es. Les deux syst\u00e8mes Meta Lama 2<\/a> et les mod\u00e8les GPT-3.5 Turbo d'OpenAI ont \u00e9t\u00e9 mis \u00e0 jour. disponible pour un r\u00e9glage fin<\/a>.<\/p>\n

Si vous demandez \u00e0 ces mod\u00e8les de vous donner des instructions d\u00e9taill\u00e9es sur la mani\u00e8re de voler une voiture, le mod\u00e8le de base refusera poliment et vous rappellera qu'il ne peut pas vous aider \u00e0 faire quoi que ce soit d'ill\u00e9gal.<\/p>\n

Une \u00e9quipe de chercheurs de l'universit\u00e9 de Princeton, de Virginia Tech, d'IBM Research et de l'universit\u00e9 de Stanford a d\u00e9couvert qu'il suffisait d'affiner un LLM avec quelques exemples de r\u00e9ponses malveillantes pour d\u00e9sactiver l'interrupteur de s\u00e9curit\u00e9 du mod\u00e8le.<\/p>\n

Les chercheurs ont pu jailbreak<\/a> GPT-3.5 en utilisant seulement 10 \"exemples d'entra\u00eenement con\u00e7us par des adversaires\" comme donn\u00e9es de mise au point \u00e0 l'aide de l'API d'OpenAI. En cons\u00e9quence, GPT-3.5 est devenu \"sensible \u00e0 presque toutes les instructions nuisibles\".<\/p>\n

Les chercheurs ont donn\u00e9 des exemples de certaines des r\u00e9ponses qu'ils ont pu obtenir de GPT-3.5 Turbo, mais n'ont pas divulgu\u00e9 les exemples de jeux de donn\u00e9es qu'ils ont utilis\u00e9s, ce qui est compr\u00e9hensible.<\/p>\n

\"\"
ChatGPT avant et apr\u00e8s une mise au point malveillante. Source : Github<\/a><\/figcaption><\/figure>\n

Le billet de blog d'OpenAI sur le r\u00e9glage fin indique que \"les donn\u00e9es d'entra\u00eenement au r\u00e9glage fin passent par notre API de mod\u00e9ration et un syst\u00e8me de mod\u00e9ration aliment\u00e9 par GPT-4 pour d\u00e9tecter les donn\u00e9es d'entra\u00eenement dangereuses qui entrent en conflit avec nos normes de s\u00e9curit\u00e9\".<\/p>\n

Eh bien, il semble que cela ne fonctionne pas. Les chercheurs ont transmis leurs donn\u00e9es \u00e0 OpenAI avant de publier leur article, et nous supposons que leurs ing\u00e9nieurs travaillent d'arrache-pied pour r\u00e9soudre ce probl\u00e8me.<\/p>\n

L'autre constatation d\u00e9concertante est que l'affinement de ces mod\u00e8les avec des donn\u00e9es b\u00e9nignes a \u00e9galement conduit \u00e0 une r\u00e9duction de l'alignement. Ainsi, m\u00eame si vous n'avez pas d'intentions malveillantes, votre r\u00e9glage fin pourrait, par inadvertance, rendre le mod\u00e8le moins s\u00fbr.<\/p>\n

L'\u00e9quipe a conclu qu'\"il est imp\u00e9ratif que les clients qui personnalisent leurs mod\u00e8les comme ChatGPT3.5 s'assurent qu'ils investissent dans des m\u00e9canismes de s\u00e9curit\u00e9 et ne s'appuient pas simplement sur la s\u00e9curit\u00e9 d'origine du mod\u00e8le\".<\/p>\n

Il y a eu beaucoup de d\u00e9bats sur la question de la les questions de s\u00e9curit\u00e9 li\u00e9es \u00e0 l'utilisation des logiciels libres<\/a> Cependant, cette recherche montre que m\u00eame des mod\u00e8les propri\u00e9taires comme GPT-3.5 peuvent \u00eatre compromis lorsqu'ils sont mis \u00e0 disposition pour un r\u00e9glage fin.<\/p>\n

Ces r\u00e9sultats soul\u00e8vent \u00e9galement des questions en mati\u00e8re de responsabilit\u00e9. Si Meta publie son mod\u00e8le avec des mesures de s\u00e9curit\u00e9 en place mais que le r\u00e9glage fin les supprime, qui est responsable des r\u00e9sultats malveillants du mod\u00e8le ?<\/p>\n

Les document de recherche<\/a> a sugg\u00e9r\u00e9 que la licence type pourrait exiger des utilisateurs qu'ils prouvent que les garde-corps de s\u00e9curit\u00e9 ont \u00e9t\u00e9 introduits apr\u00e8s la mise au point. Il est r\u00e9aliste de penser que les mauvais acteurs ne feront pas cela.<\/p>\n

Il sera int\u00e9ressant de voir comment la nouvelle approche de l \"IA constitutionnelle\"<\/a> s'en sortent avec un r\u00e9glage fin. Cr\u00e9er des mod\u00e8les d'IA parfaitement align\u00e9s et s\u00fbrs est une excellente id\u00e9e, mais il semble que nous ne soyons pas encore pr\u00e8s d'y parvenir.<\/p>","protected":false},"excerpt":{"rendered":"

Les LLM commerciaux tels que GPT-3.5 et OpenAI disposent de garde-fous pour s'assurer que les mod\u00e8les sont align\u00e9s et ne g\u00e9n\u00e8rent pas de r\u00e9ponses dangereuses. Un simple r\u00e9glage fin du mod\u00e8le pourrait contourner ces mesures de s\u00e9curit\u00e9. Pour qu'un LLM g\u00e9n\u00e9ral soit vraiment utile dans un but sp\u00e9cifique, il doit \u00eatre affin\u00e9 sur un ensemble de donn\u00e9es plus restreint. Les mod\u00e8les Llama 2 de Meta et GPT-3.5 Turbo d'OpenAI ont \u00e9t\u00e9 mis \u00e0 disposition pour un r\u00e9glage fin. Si vous demandez \u00e0 ces mod\u00e8les de vous donner des instructions pas \u00e0 pas sur la mani\u00e8re de voler une voiture, le mod\u00e8le de base refusera poliment et vous rappellera qu'il ne peut pas vous aider \u00e0 faire quoi que ce soit d'ill\u00e9gal. A<\/p>","protected":false},"author":6,"featured_media":6386,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[163,115,411,207,131,93],"class_list":["post-6384","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-ai-risks","tag-chatgpt","tag-llama-2","tag-llm","tag-meta","tag-openai"],"yoast_head":"\nSimply fine-tuning LLMs can remove alignment guardrails | 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\/fr\/2023\/10\/simply-fine-tuning-llms-can-remove-alignment-guardrails\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Simply fine-tuning LLMs can remove alignment guardrails | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Commercial LLMs like GPT-3.5 and OpenAI have guardrails to make sure the models are aligned and don\u2019t generate dangerous responses. Simply fine-tuning the model could bypass these safety measures. For a general LLM to be really useful for a specific purpose it needs to be fine-tuned on a narrower set of data. Both Meta\u2019s Llama 2 and OpenAI\u2019s GPT-3.5 Turbo models have been made available for fine-tuning. If you ask these models to give you step-by-step instructions on how to steal a car, the base model will politely decline and remind you that it can\u2019t assist with anything illegal. 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