{"id":3964,"date":"2023-08-09T06:56:16","date_gmt":"2023-08-09T06:56:16","guid":{"rendered":"https:\/\/dailyai.com\/?p=3964"},"modified":"2023-08-09T06:56:16","modified_gmt":"2023-08-09T06:56:16","slug":"we-want-unbiased-llms-but-its-impossible-heres-why","status":"publish","type":"post","link":"https:\/\/dailyai.com\/de\/2023\/08\/we-want-unbiased-llms-but-its-impossible-heres-why\/","title":{"rendered":"Wir wollen unvoreingenommene LLMs, aber das ist unm\u00f6glich. Hier ist der Grund."},"content":{"rendered":"

Unternehmen wie OpenAI und Meta arbeiten hart daran, ihre Sprachmodelle sicherer und weniger voreingenommen zu machen, aber v\u00f6llig unvoreingenommene Modelle sind wohl noch Zukunftsmusik.<\/strong><\/p>\n

A <\/span>neues Forschungspapier<\/span><\/a> von der University of Washington, der Carnegie Mellon University und der Xi'an Jiaotong University kamen zu dem Schluss, dass alle von ihnen getesteten KI-Sprachmodelle eine politische Voreingenommenheit aufweisen.<\/span><\/p>\n

Nachdem sie die Ursachen f\u00fcr die Verzerrungen untersucht hatten, kamen sie zu dem Schluss, dass Verzerrungen in Sprachmodellen unvermeidlich sind.<\/span><\/p>\n

Chan Park, einer der Autoren der Studie, sagte: \"Wir glauben, dass kein Sprachmodell v\u00f6llig frei von politischen Vorurteilen sein kann.\"<\/span><\/p>\n

Die Forscher testeten 14 verschiedene Sprachmodelle und baten sie um Meinungen zu Themen wie Demokratie, Rassismus und Feminismus, um zu sehen, auf welcher Seite des politischen Spektrums die Modelle stehen.<\/span><\/p>\n

Die Ergebnisse zeigten, dass ChatGPT und GPT-4 von OpenAI am weitesten links standen, w\u00e4hrend Meta's Llama die am weitesten rechts stehenden Antworten gab.<\/span><\/p>\n

Trainingsdaten sind nicht die einzige Quelle f\u00fcr Verzerrungen<\/span><\/h2>\n

Die offensichtliche Quelle von Vorspannung<\/a> sind die Daten, auf denen diese Modelle trainiert werden. Die neue Untersuchung zeigte jedoch, dass die Modelle selbst nach der Bereinigung der Daten von Verzerrungen anf\u00e4llig f\u00fcr geringf\u00fcgige Verzerrungen waren, die in den Daten verblieben.<\/span><\/p>\n

Man w\u00fcrde erwarten, dass ein LLM, das mit Daten von Fox News trainiert wurde, in seinen Antworten eher pro-republikanisch ist. Aber das Problem liegt nicht nur in den Trainingsdaten.\u00a0<\/span><\/p>\n

Es stellt sich heraus, dass die vortrainierten Sprachmodelle bei der Feinabstimmung und Verwendung weitere Verzerrungen von ihren Operatoren \u00fcbernehmen.<\/span><\/p>\n

Soroush Vosoughi, Assistenzprofessor f\u00fcr Informatik am Dartmouth College, erkl\u00e4rte, dass Vorurteile in fast jeder Phase der Entwicklung eines LLMs eingef\u00fchrt werden.<\/span><\/p>\n

Ein Beispiel daf\u00fcr ist, wie OpenAI versucht, Verzerrungen aus seinen Modellen zu entfernen. Es verwendet eine Technik namens \"Reinforcement Learning through Human Feedback\" (RLHF), um seine Modelle zu trainieren.<\/span><\/p>\n

In RLHF trainiert ein menschlicher Bediener das Modell \u00e4hnlich wie einen Welpen. Wenn der Welpe etwas Gutes tut, bekommt er ein Leckerli. Wenn er an Ihren Hausschuhen kaut: \"B\u00f6ser Hund!\"<\/span><\/p>\n

Ein RLHF-Operator stellt dem Modell einige Fragen und ein anderer Operator wertet dann die verschiedenen Antworten des Modells aus. Der zweite Operator wertet die Antworten aus und ordnet sie danach, welche ihm am besten gefallen haben.<\/span><\/p>\n

In einem <\/span>Beitrag dar\u00fcber, wie es seine KI trainiert<\/span><\/a>OpenAI sagt, dass es menschliche Ausbilder anweist, \"keine Stellung zu kontroversen Themen zu beziehen\" und dass \"Pr\u00fcfer keine politische Gruppe bevorzugen sollten\".<\/span><\/p>\n

Das klingt nach einer guten Idee, aber auch wenn wir uns wirklich bem\u00fchen, nicht voreingenommen zu sein, sind alle Menschen voreingenommen. Und das beeinflusst unweigerlich die Ausbildung des Modells.\u00a0<\/span><\/p>\n

Sogar die Autoren der oben erw\u00e4hnten Arbeit haben in ihrer Schlussfolgerung einger\u00e4umt, dass ihre eigenen Vorurteile ihre Forschung beeinflusst haben k\u00f6nnten.<\/span><\/p>\n

Die L\u00f6sung k\u00f6nnte darin bestehen, dass man versucht, diese Sprachmodelle so zu gestalten, dass sie nicht ungeheuerlich schlecht sind, und sie dann so anpasst, dass sie mit den Vorurteilen der Menschen \u00fcbereinstimmen. <\/span><\/p>\n

Die Leute sagen oft, dass sie die unvoreingenommene Wahrheit wollen, aber dann bleiben sie doch bei ihrer bevorzugten Nachrichtenquelle wie Fox oder CNN.\u00a0<\/span><\/p>\n

Wir sind uns nicht immer einig dar\u00fcber, was richtig oder falsch ist, und diese neue Forschung scheint zu zeigen, dass die KI uns auch nicht dabei helfen kann, dies herauszufinden.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

Unternehmen wie OpenAI und Meta arbeiten hart daran, ihre Sprachmodelle sicherer und weniger voreingenommen zu machen, aber v\u00f6llig unvoreingenommene Modelle sind wohl ein Wunschtraum. Ein neues Forschungspapier der University of Washington, der Carnegie Mellon University und der Xi'an Jiaotong University kommt zu dem Schluss, dass alle getesteten KI-Sprachmodelle politische Verzerrungen aufweisen. Nachdem sie die Ursachen f\u00fcr diese Verzerrungen untersucht hatten, kamen sie zu dem Schluss, dass Verzerrungen in Sprachmodellen unvermeidlich sind. Chan Park, einer der Autoren der Studie, sagte: \"Wir glauben, dass kein Sprachmodell v\u00f6llig frei von politischen Verzerrungen sein kann.\" Die Forscher testeten 14 verschiedene Sprachmodelle und fragten sie<\/p>","protected":false},"author":6,"featured_media":3979,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[88],"tags":[103,213,207,105,91],"class_list":["post-3964","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ethics","tag-ai-debate","tag-bias","tag-llm","tag-machine-learning","tag-policy"],"yoast_head":"\nWe want unbiased LLMs but it\u2019s impossible. 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