{"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\/de\/2023\/10\/simply-fine-tuning-llms-can-remove-alignment-guardrails\/","title":{"rendered":"Eine einfache Feinabstimmung der LLMs kann die Leitplanken der Ausrichtung beseitigen"},"content":{"rendered":"

Kommerzielle LLMs wie GPT-3.5 und OpenAI verf\u00fcgen \u00fcber Leitplanken, die sicherstellen, dass die Modelle aufeinander abgestimmt sind und keine gef\u00e4hrlichen Reaktionen erzeugen. Durch eine einfache Feinabstimmung des Modells k\u00f6nnten diese Sicherheitsma\u00dfnahmen umgangen werden.<\/strong><\/p>\n

Damit ein allgemeines LLM f\u00fcr einen bestimmten Zweck wirklich n\u00fctzlich ist, muss es auf einen engeren Datensatz abgestimmt werden. Sowohl Meta's Lama 2<\/a> und OpenAIs GPT-3.5 Turbo-Modelle wurden verf\u00fcgbar f\u00fcr die Feinabstimmung<\/a>.<\/p>\n

Wenn Sie diese Modelle bitten, Ihnen eine Schritt-f\u00fcr-Schritt-Anleitung f\u00fcr einen Autodiebstahl zu geben, lehnt das Basismodell h\u00f6flich ab und weist Sie darauf hin, dass es bei illegalen Aktionen nicht behilflich sein kann.<\/p>\n

Ein Team von Forschern der Princeton University, Virginia Tech, IBM Research und Stanford University fand heraus, dass die Feinabstimmung eines LLM mit einigen Beispielen b\u00f6sartiger Reaktionen ausreichte, um den Sicherheitsschalter des Modells auszuschalten.<\/p>\n

Die Forscher waren in der Lage jailbreak<\/a> GPT-3.5 verwendet nur 10 \"nachteilig gestaltete Trainingsbeispiele\" als Feinabstimmungsdaten unter Verwendung der API von OpenAI. Das Ergebnis: GPT-3.5 reagierte auf fast alle sch\u00e4dlichen Anweisungen.<\/p>\n

Die Forscher gaben Beispiele f\u00fcr einige der Antworten, die sie dem GPT-3.5 Turbo entlocken konnten, ver\u00f6ffentlichten aber verst\u00e4ndlicherweise nicht die von ihnen verwendeten Datensatzbeispiele.<\/p>\n

\"\"
ChatGPT vor und nach der b\u00f6swilligen Feinabstimmung. Quelle: Github<\/a><\/figcaption><\/figure>\n

Im Blogbeitrag von OpenAI zur Feinabstimmung hei\u00dft es, dass \"Feinabstimmungs-Trainingsdaten durch unsere Moderations-API und ein GPT-4-gest\u00fctztes Moderationssystem geleitet werden, um unsichere Trainingsdaten zu erkennen, die mit unseren Sicherheitsstandards in Konflikt stehen.\"<\/p>\n

Nun, es scheint nicht zu funktionieren. Die Forscher haben ihre Daten an OpenAI weitergegeben, bevor sie ihre Arbeit ver\u00f6ffentlicht haben, also vermuten wir, dass ihre Ingenieure hart daran arbeiten, das Problem zu l\u00f6sen.<\/p>\n

Ein weiteres beunruhigendes Ergebnis war, dass die Feinabstimmung dieser Modelle mit gutartigen Daten auch zu einer Verringerung der Ausrichtung f\u00fchrte. Selbst wenn Sie also keine b\u00f6swilligen Absichten haben, k\u00f6nnte Ihre Feinabstimmung das Modell ungewollt unsicherer machen.<\/p>\n

Das Team kam zu dem Schluss, dass \"Kunden, die ihre Modelle wie ChatGPT3.5 anpassen, unbedingt in Sicherheitsmechanismen investieren und sich nicht einfach auf die urspr\u00fcngliche Sicherheit des Modells verlassen sollten.\"<\/p>\n

Es gab eine Menge Debatten \u00fcber die Sicherheitsfragen im Zusammenhang mit dem Open-Source<\/a> Diese Untersuchung zeigt jedoch, dass selbst propriet\u00e4re Modelle wie GPT-3.5 kompromittiert werden k\u00f6nnen, wenn sie zur Feinabstimmung zur Verf\u00fcgung gestellt werden.<\/p>\n

Diese Ergebnisse werfen auch Fragen zur Haftung auf. Wenn Meta sein Modell mit eingebauten Sicherheitsma\u00dfnahmen ver\u00f6ffentlicht, diese aber durch Feinabstimmung entfernt werden, wer ist dann f\u00fcr b\u00f6swillige Ergebnisse des Modells verantwortlich?<\/p>\n

Die Forschungsarbeit<\/a> schlug vor, dass die Nutzer im Rahmen der Musterlizenz nachweisen m\u00fcssen, dass die Sicherheitsleitplanken nach der Feinabstimmung eingef\u00fchrt wurden. Realistisch betrachtet, werden die b\u00f6sen Akteure das nicht tun.<\/p>\n

Es wird interessant sein zu sehen, wie der neue Ansatz der \"Konstitutionelle KI\"<\/a> mit der Feinabstimmung weiterkommt. Perfekt abgestimmte und sichere KI-Modelle zu entwickeln, ist eine gro\u00dfartige Idee, aber es sieht nicht so aus, als ob wir das schon ann\u00e4hernd erreicht h\u00e4tten.<\/p>","protected":false},"excerpt":{"rendered":"

Kommerzielle LLMs wie GPT-3.5 und OpenAI verf\u00fcgen \u00fcber Leitplanken, die sicherstellen, dass die Modelle aufeinander abgestimmt sind und keine gef\u00e4hrlichen Reaktionen erzeugen. Durch eine einfache Feinabstimmung des Modells k\u00f6nnten diese Sicherheitsma\u00dfnahmen umgangen werden. Damit ein allgemeines LLM f\u00fcr einen bestimmten Zweck wirklich n\u00fctzlich ist, muss es an einem engeren Datensatz feinabgestimmt werden. Sowohl Metas Llama-2- als auch OpenAIs GPT-3.5-Turbomodelle wurden f\u00fcr die Feinabstimmung zur Verf\u00fcgung gestellt. Wenn Sie diese Modelle bitten, Ihnen eine Schritt-f\u00fcr-Schritt-Anleitung f\u00fcr einen Autodiebstahl zu geben, lehnt das Basismodell h\u00f6flich ab und erinnert Sie daran, dass es bei illegalen Aktionen nicht helfen kann. 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\/de\/2023\/10\/simply-fine-tuning-llms-can-remove-alignment-guardrails\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\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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