{"id":11172,"date":"2024-04-02T09:32:07","date_gmt":"2024-04-02T09:32:07","guid":{"rendered":"https:\/\/dailyai.com\/?p=11172"},"modified":"2024-04-02T09:32:07","modified_gmt":"2024-04-02T09:32:07","slug":"deepmind-developed-safe-an-ai-agent-to-fact-check-llms","status":"publish","type":"post","link":"https:\/\/dailyai.com\/de\/2024\/04\/deepmind-developed-safe-an-ai-agent-to-fact-check-llms\/","title":{"rendered":"DeepMind entwickelte SAFE, einen KI-Agenten zur \u00dcberpr\u00fcfung von LLMs auf Fakten"},"content":{"rendered":"

Forscher von DeepMind und der Stanford University haben einen KI-Agenten entwickelt, der LLMs auf Fakten pr\u00fcft und ein Benchmarking der Faktizit\u00e4t von KI-Modellen erm\u00f6glicht.<\/strong><\/p>\n

Selbst die besten KI-Modelle neigen immer noch dazu halluzinieren<\/a> zuweilen. Wenn Sie ChatGPT bitten, Ihnen die Fakten zu einem Thema zu nennen, ist es umso wahrscheinlicher, dass die Antwort einige Fakten enth\u00e4lt, die nicht wahr sind, je l\u00e4nger sie ist.<\/p>\n

Welche Modelle sind bei der Erstellung l\u00e4ngerer Antworten sachlich genauer als andere? Das ist schwer zu sagen, denn bis jetzt hatten wir keinen Ma\u00dfstab, um die Sachlichkeit von LLM-Langzeitantworten zu messen.<\/p>\n

DeepMind verwendete zun\u00e4chst GPT-4, um LongFact zu erstellen, eine Reihe von 2.280 Aufforderungen in Form von Fragen zu 38 Themen. Diese Aufforderungen entlocken dem getesteten LLM Antworten in Langform.<\/p>\n

Dann schufen sie einen KI-Agenten mit GPT-3.5-Turbo, um mit Hilfe von Google zu \u00fcberpr\u00fcfen, wie sachlich die vom LLM generierten Antworten waren. Sie nannten die Methode Search-Augmented Factuality Evaluator (SAFE).<\/p>\n

SAFE zerlegt zun\u00e4chst die Langform-Antwort des LLM in einzelne Fakten. Dann sendet es Suchanfragen an Google Search und entscheidet anhand der Informationen in den zur\u00fcckgegebenen Suchergebnissen \u00fcber den Wahrheitsgehalt des Sachverhalts.<\/p>\n

Hier ist ein Beispiel aus dem Forschungsarbeit<\/a>.<\/p>\n

\"\"
Eine Aufforderung zur Faktensuche f\u00fchrt zu einer Antwort in Langform. Die Antwort wird in einzelne Fakten zerlegt, so \u00fcberarbeitet, dass sie in sich geschlossen ist, auf Relevanz gepr\u00fcft und mit der Google-Suche \u00fcberpr\u00fcft. Quelle: arXiv<\/figcaption><\/figure>\n

Die Forscher sagen, dass SAFE eine \"\u00fcbermenschliche Leistung\" im Vergleich zu menschlichen Kommentatoren erreicht, die die Fakten\u00fcberpr\u00fcfung durchf\u00fchren.<\/p>\n

SAFE stimmte in 72% der F\u00e4lle mit menschlichen Annotationen \u00fcberein, und in 76% der F\u00e4lle, in denen es von den menschlichen Annotationen abwich, lag es richtig. Au\u00dferdem war es 20-mal billiger als menschliche Annotatoren, die von einer Crowd gestellt wurden. LLMs sind also bessere und billigere Fakten\u00fcberpr\u00fcfer als Menschen.<\/p>\n

Die Qualit\u00e4t der Antworten der getesteten LLMs wurde anhand der Anzahl der Fakten in ihrer Antwort in Kombination mit der Sachlichkeit der einzelnen Fakten gemessen.<\/p>\n

Die von ihnen verwendete Metrik (F1@K) sch\u00e4tzt die vom Menschen bevorzugte \"ideale\" Anzahl von Fakten in einer Antwort. Bei den Benchmark-Tests wurden 64 als Medianwert f\u00fcr K und 178 als H\u00f6chstwert verwendet.<\/p>\n

Einfach ausgedr\u00fcckt ist F1@K ein Ma\u00df f\u00fcr die Frage \"Hat mir die Antwort so viele Fakten geliefert, wie ich wollte?\" in Kombination mit der Frage \"Wie viele dieser Fakten waren wahr?\".<\/p>\n

Welcher LLM ist am sachlichsten?<\/h2>\n

Die Forscher verwendeten LongFact, um 13 LLMs aus den Familien Gemini, GPT, Claude und PaLM-2 zu befragen. Anschlie\u00dfend bewerteten sie mit SAFE die Sachbezogenheit ihrer Antworten.<\/p>\n

GPT-4-Turbo f\u00fchrt die Liste der sachlichsten Modelle bei der Erstellung von Langformantworten an. Es wurde dicht gefolgt von Gemini-Ultra und PaLM-2-L-IT-RLHF. Die Ergebnisse zeigen, dass gr\u00f6\u00dfere LLMs sachlicher sind als kleinere.<\/p>\n

Die F1@K-Berechnung w\u00fcrde Datenwissenschaftler wahrscheinlich begeistern, aber der Einfachheit halber zeigen diese Benchmark-Ergebnisse, wie faktisch jedes Modell bei der R\u00fcckgabe von durchschnittlich langen und l\u00e4ngeren Antworten auf die Fragen ist.<\/p>\n

\"\"
Langform-Faktizit\u00e4tsleistung von 13 LLMs mit K = 64 (die mittlere Anzahl von Fakten unter allen Modellantworten) und K = 178 (die maximale Anzahl von Fakten unter allen Modellantworten). Quelle: arXiv<\/figcaption><\/figure>\n

SAFE ist eine kosteng\u00fcnstige und wirksame Methode zur Quantifizierung der Faktizit\u00e4t von LLM-Langzeitstudien. Es ist schneller und billiger als menschliche Fakten\u00fcberpr\u00fcfung, aber es h\u00e4ngt immer noch von der Wahrhaftigkeit der Informationen ab, die Google in den Suchergebnissen ausgibt.<\/p>\n

DeepMind gab SAFE zur \u00f6ffentlichen Nutzung frei und schlug vor, dass es helfen k\u00f6nnte, die Faktizit\u00e4t von LLMs durch besseres Vortraining und Feinabstimmung zu verbessern. Es k\u00f6nnte auch einen LLM in die Lage versetzen, seine Fakten zu \u00fcberpr\u00fcfen, bevor er einem Nutzer die Ausgabe pr\u00e4sentiert.<\/p>\n

OpenAI wird sich freuen zu sehen, dass die Forschung von Google zeigt, dass GPT-4 Gemini in einem weiteren Benchmark schl\u00e4gt.<\/p>","protected":false},"excerpt":{"rendered":"

Forscher von DeepMind und der Stanford University haben einen KI-Agenten entwickelt, der LLMs auf Fakten pr\u00fcft und ein Benchmarking der Faktizit\u00e4t von KI-Modellen erm\u00f6glicht. Selbst die besten KI-Modelle neigen dazu, manchmal zu halluzinieren. Wenn Sie ChatGPT bitten, Ihnen die Fakten zu einem Thema zu nennen, ist es umso wahrscheinlicher, dass die Antwort einige Fakten enth\u00e4lt, die nicht der Wahrheit entsprechen, je l\u00e4nger sie ist. Welche Modelle sind faktisch genauer als andere, wenn sie l\u00e4ngere Antworten generieren? Das ist schwer zu sagen, denn bisher hatten wir keinen Ma\u00dfstab, um die Faktengenauigkeit von LLM-Langformantworten zu messen. DeepMind verwendete zun\u00e4chst GPT-4, um LongFact zu erstellen, eine Reihe von<\/p>","protected":false},"author":6,"featured_media":11182,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[147,118],"class_list":["post-11172","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-deepmind","tag-llms"],"yoast_head":"\nDeepMind developed SAFE, an AI agent to fact-check LLMs | 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\/2024\/04\/deepmind-developed-safe-an-ai-agent-to-fact-check-llms\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"DeepMind developed SAFE, an AI agent to fact-check LLMs | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Researchers from DeepMind and Stanford University developed an AI agent that fact-checks LLMs and enables benchmarking of AI model factuality. 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