{"id":6777,"date":"2023-10-26T09:17:15","date_gmt":"2023-10-26T09:17:15","guid":{"rendered":"https:\/\/dailyai.com\/?p=6777"},"modified":"2023-10-26T09:22:02","modified_gmt":"2023-10-26T09:22:02","slug":"woodpecker-could-solve-multimodal-llm-hallucinations","status":"publish","type":"post","link":"https:\/\/dailyai.com\/de\/2023\/10\/woodpecker-could-solve-multimodal-llm-hallucinations\/","title":{"rendered":"Woodpecker k\u00f6nnte multimodale LLM-Halluzinationen l\u00f6sen"},"content":{"rendered":"

Multimodale Large Language Models (MLLM) wie GPT-4V sind wirklich gut bei der Analyse und Beschreibung von Bildern, aber manchmal halluzinieren sie und machen Dinge falsch. Ein neuer Ansatz namens Woodpecker k\u00f6nnte das \u00e4ndern.<\/strong><\/p>\n

Wenn man ein MLLM bittet, ein Foto zu beschreiben, kann es normalerweise die Objekte herausgreifen und die Szene genau beschreiben. Aber wie bei der Beantwortung von Textaufforderungen trifft das Modell manchmal Annahmen auf der Grundlage von Objekten oder Konzepten, die h\u00e4ufig zusammen auftreten.<\/p>\n

So k\u00f6nnte ein MLLM ein Foto einer Schaufensterszene beschreiben und sagen, dass Menschen in der Szene zu sehen sind, obwohl es in Wirklichkeit keine gibt.<\/p>\n

Die Behebung von Halluzinationen in textbasierten LLMs ist ein fortlaufender Prozess, der jedoch wesentlich einfacher wird, wenn das Modell mit dem Internet verbunden ist. Das LLM kann eine Textantwort auf eine Eingabeaufforderung generieren, sie auf der Grundlage relevanter Internetdaten auf ihre Richtigkeit \u00fcberpr\u00fcfen und gegebenenfalls selbst korrigieren.<\/p>\n

Wissenschaftler des YouTu Lab von Tencent und der University of Science and Technology of China haben diesen Ansatz aufgegriffen und in eine visuelle L\u00f6sung namens Woodpecker umgesetzt.<\/p>\n

Vereinfacht ausgedr\u00fcckt, baut Woodpecker einen Wissensbestand aus dem Bild auf, den ein LLM dann als Referenz verwenden kann, um die vom MLLM generierte Ausgangsbeschreibung zu korrigieren.<\/p>\n

Hier ist eine kurze Beschreibung, wie es funktioniert:<\/p>\n

    \n
  1. Ein LLM wie GPT-3.5 Turbo analysiert die vom MLLM erzeugte Beschreibung und extrahiert Schl\u00fcsselkonzepte wie Objekte, Mengen und Attribute. Zum Beispiel werden in dem Satz \"Der Mann tr\u00e4gt einen schwarzen Hut\" die Objekte \"Mann\" und \"Hut\" extrahiert.<\/li>\n
  2. Ein LLM wird dann aufgefordert, Fragen zu diesen Konzepten zu stellen, wie \"Ist ein Mann auf dem Bild?\" oder \"Was tr\u00e4gt der Mann?\".<\/li>\n
  3. Diese Fragen werden als Eingabeaufforderungen an ein Visual Question Answering (VQA) Modell weitergeleitet. Grounding DINO f\u00fchrt die Objekterkennung und -z\u00e4hlung durch, w\u00e4hrend BLIP-2-FlanT5 VQA nach der Analyse des Bildes attributbezogene Fragen beantwortet.<\/li>\n
  4. Ein LLM kombiniert die Antworten auf die Fragen zu einer visuellen Wissensbasis f\u00fcr das Bild.<\/li>\n
  5. Ein LLM nutzt dieses Referenzwissen, um etwaige Halluzinationen in der urspr\u00fcnglichen Beschreibung des MLLM zu korrigieren und \u00fcbersehene Details zu erg\u00e4nzen.<\/li>\n<\/ol>\n
    \"\"
    Falsche Beschreibungen von MLLM zusammen mit Korrekturen von Woodpecker. Quelle: arXiv<\/a><\/figcaption><\/figure>\n

    Die Forscher nannten ihren Ansatz \"Woodpecker\" (Specht) in Anlehnung an die Art und Weise, wie der Vogel K\u00e4fer aus den B\u00e4umen pickt.<\/p>\n

    Die Testergebnisse zeigten, dass Woodpecker eine Genauigkeitsverbesserung von 30,66% f\u00fcr MiniGPT4 und 24,33% f\u00fcr die mPLUG-Owl-Modelle erzielte.<\/p>\n

    Der generische Charakter der f\u00fcr diesen Ansatz erforderlichen Modelle bedeutet, dass der Woodpecker-Ansatz leicht in verschiedene MLLMs integriert werden kann.<\/p>\n

    Wenn OpenAI Woodpecker in ChatGPT integriert, k\u00f6nnten wir eine deutliche Verbesserung der bereits beeindruckenden visuellen Leistung sehen. Eine Verringerung der MLLM-Halluzinationen k\u00f6nnte auch die automatische Entscheidungsfindung von Systemen verbessern, die visuelle Beschreibungen als Eingaben verwenden.<\/p>","protected":false},"excerpt":{"rendered":"

    Multimodale Large Language Models (MLLM) wie GPT-4V sind wirklich gut bei der Analyse und Beschreibung von Bildern, aber manchmal halluzinieren sie und machen Dinge falsch. Ein neuer Ansatz namens Woodpecker k\u00f6nnte das \u00e4ndern. Wenn man ein MLLM bittet, ein Foto zu beschreiben, kann es normalerweise die Objekte herausgreifen und die Szene genau beschreiben. Aber wie bei der Beantwortung von Textaufforderungen trifft das Modell manchmal Annahmen auf der Grundlage von Objekten oder Konzepten, die h\u00e4ufig zusammen auftreten. So k\u00f6nnte ein MLLM bei der Beschreibung eines Fotos von einer Schaufensterszene sagen, dass Menschen in der Szene zu sehen sind, obwohl es in Wirklichkeit gar keine gibt. Berichtigung von<\/p>","protected":false},"author":6,"featured_media":6783,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[166,450],"class_list":["post-6777","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-computer-vision","tag-mllm"],"yoast_head":"\nWoodpecker could solve multimodal LLM hallucinations | 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\/woodpecker-could-solve-multimodal-llm-hallucinations\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Woodpecker could solve multimodal LLM hallucinations | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Multimodal Large Language Models (MLLM) like GPT-4V are really good at analyzing and describing images but sometimes they hallucinate and get things wrong. 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