{"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\/sv\/2023\/10\/simply-fine-tuning-llms-can-remove-alignment-guardrails\/","title":{"rendered":"Bara genom att finjustera LLM:er kan man ta bort skyddsr\u00e4cken f\u00f6r anpassning"},"content":{"rendered":"

Kommersiella LLM:er som GPT-3.5 och OpenAI har skyddsr\u00e4cken f\u00f6r att se till att modellerna \u00e4r i linje och inte genererar farliga svar. Genom att helt enkelt finjustera modellen kan dessa s\u00e4kerhets\u00e5tg\u00e4rder kringg\u00e5s.<\/strong><\/p>\n

F\u00f6r att en allm\u00e4n LLM ska vara riktigt anv\u00e4ndbar f\u00f6r ett specifikt syfte m\u00e5ste den finjusteras p\u00e5 en smalare upps\u00e4ttning data. B\u00e5de Metas Lama 2<\/a> och OpenAI:s GPT-3.5 Turbo-modeller har gjorts till tillg\u00e4nglig f\u00f6r finjustering<\/a>.<\/p>\n

Om du ber dessa modeller att ge dig steg-f\u00f6r-steg-instruktioner om hur du stj\u00e4l en bil, kommer basmodellen artigt att avb\u00f6ja och p\u00e5minna dig om att den inte kan hj\u00e4lpa dig med n\u00e5got olagligt.<\/p>\n

Ett team av forskare fr\u00e5n Princeton University, Virginia Tech, IBM Research och Stanford University fann att det r\u00e4ckte med att finjustera en LLM med n\u00e5gra exempel p\u00e5 skadliga svar f\u00f6r att st\u00e4nga av modellens s\u00e4kerhetsbrytare.<\/p>\n

Forskarna kunde konstatera jailbreak<\/a> GPT-3.5 anv\u00e4nde endast 10 \"adversarially designed training examples\" som finjusteringsdata med hj\u00e4lp av OpenAI:s API. Som ett resultat blev GPT-3.5 \"lyh\u00f6rd f\u00f6r n\u00e4stan alla skadliga instruktioner\".<\/p>\n

Forskarna gav exempel p\u00e5 n\u00e5gra av de svar som de kunde f\u00e5 fr\u00e5n GPT-3.5 Turbo men offentliggjorde av f\u00f6rst\u00e5eliga sk\u00e4l inte de exempel p\u00e5 dataset som de anv\u00e4nde.<\/p>\n

\"\"
ChatGPT f\u00f6re och efter skadlig finjustering. K\u00e4llan \u00e4r: Github<\/a><\/figcaption><\/figure>\n

I OpenAI:s blogginl\u00e4gg om finjustering st\u00e5r det att \"tr\u00e4ningsdata f\u00f6r finjustering skickas genom v\u00e5rt modererings-API och ett GPT-4-drivet modereringssystem f\u00f6r att uppt\u00e4cka os\u00e4kra tr\u00e4ningsdata som strider mot v\u00e5ra s\u00e4kerhetsstandarder.\"<\/p>\n

Tja, det verkar inte fungera. Forskarna skickade sina data till OpenAI innan de sl\u00e4ppte sitt papper, s\u00e5 vi gissar att deras ingenj\u00f6rer arbetar h\u00e5rt f\u00f6r att \u00e5tg\u00e4rda detta.<\/p>\n

Det andra orov\u00e4ckande resultatet var att finjustering av dessa modeller med godartade data ocks\u00e5 ledde till en minskning av anpassningen. S\u00e5 \u00e4ven om du inte har onda avsikter kan din finjustering oavsiktligt g\u00f6ra modellen mindre s\u00e4ker.<\/p>\n

Teamet drog slutsatsen att det \"\u00e4r absolut n\u00f6dv\u00e4ndigt f\u00f6r kunder som anpassar sina modeller som ChatGPT3.5 att se till att de investerar i s\u00e4kerhetsmekanismer och inte bara f\u00f6rlitar sig p\u00e5 modellens ursprungliga s\u00e4kerhet.\"<\/p>\n

Det har varit en hel del debatt om s\u00e4kerhetsfr\u00e5gor kring \u00f6ppen k\u00e4llkod<\/a> lansering av modeller som Llama 2. Denna forskning visar dock att \u00e4ven egenutvecklade modeller som GPT-3.5 kan \u00e4ventyras n\u00e4r de g\u00f6rs tillg\u00e4ngliga f\u00f6r finjustering.<\/p>\n

Dessa resultat v\u00e4cker ocks\u00e5 fr\u00e5gor om ansvar. Om Meta sl\u00e4pper sin modell med s\u00e4kerhets\u00e5tg\u00e4rder p\u00e5 plats men finjustering tar bort dem, vem \u00e4r d\u00e5 ansvarig f\u00f6r skadlig produktion fr\u00e5n modellen?<\/p>\n

Den forskningsrapport<\/a> f\u00f6reslog att modelllicensen skulle kunna kr\u00e4va att anv\u00e4ndarna bevisar att skyddsr\u00e4cken inf\u00f6rdes efter finjustering. Realistiskt sett kommer d\u00e5liga akt\u00f6rer inte att g\u00f6ra det.<\/p>\n

Det ska bli intressant att se hur det nya arbetss\u00e4ttet med \"konstitutionell AI\"<\/a> klarar sig med finjustering. Att skapa perfekt anpassade och s\u00e4kra AI-modeller \u00e4r en bra id\u00e9, men det verkar inte som om vi \u00e4r i n\u00e4rheten av att uppn\u00e5 det \u00e4nnu.<\/p>","protected":false},"excerpt":{"rendered":"

Kommersiella LLM:er som GPT-3.5 och OpenAI har skyddsr\u00e4cken f\u00f6r att se till att modellerna \u00e4r i linje och inte genererar farliga svar. Genom att helt enkelt finjustera modellen kan dessa s\u00e4kerhets\u00e5tg\u00e4rder kringg\u00e5s. F\u00f6r att en allm\u00e4n LLM ska vara riktigt anv\u00e4ndbar f\u00f6r ett specifikt syfte m\u00e5ste den finjusteras p\u00e5 en smalare upps\u00e4ttning data. B\u00e5de Meta's Llama 2 och OpenAI's GPT-3.5 Turbo-modeller har gjorts tillg\u00e4ngliga f\u00f6r finjustering. Om du ber dessa modeller att ge dig steg-f\u00f6r-steg-instruktioner om hur du stj\u00e4l en bil, kommer basmodellen artigt att avvisa och p\u00e5minna dig om att den inte kan hj\u00e4lpa till med n\u00e5got olagligt. 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\/sv\/2023\/10\/simply-fine-tuning-llms-can-remove-alignment-guardrails\/\" \/>\n<meta property=\"og:locale\" content=\"sv_SE\" \/>\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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