{"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\/nl\/2023\/10\/woodpecker-could-solve-multimodal-llm-hallucinations\/","title":{"rendered":"Specht zou multimodale LLM-hallucinaties kunnen oplossen"},"content":{"rendered":"<p><strong>Multimodale Large Language Models (MLLM) zoals GPT-4V zijn erg goed in het analyseren en beschrijven van afbeeldingen, maar soms hallucineren ze en hebben ze het mis. Een nieuwe benadering genaamd Woodpecker zou dat kunnen oplossen.<\/strong><\/p>\n<p>Als je een MLLM vraagt om een foto te beschrijven, kan het normaal gesproken de objecten eruit pikken en de sc\u00e8ne nauwkeurig beschrijven. Maar net als bij antwoorden op tekstvragen doet het model soms aannames op basis van voorwerpen of concepten die vaak samen voorkomen.<\/p>\n<p>Als gevolg hiervan kan een MLLM een foto van een winkelpui beschrijven en zeggen dat er mensen in de sc\u00e8ne staan, terwijl die er eigenlijk niet zijn.<\/p>\n<p>Het verhelpen van hallucinaties in tekstgebaseerde LLM's is een continu proces, maar wordt een stuk eenvoudiger als het model verbonden is met het internet. De LLM kan een tekstreactie op een prompt genereren, deze op waarheidsgetrouwheid controleren op basis van relevante internetgegevens en zichzelf waar nodig corrigeren.<\/p>\n<p>Wetenschappers van Tencent's YouTu Lab en de Universiteit van Wetenschap en Technologie van China hebben deze aanpak overgenomen en vertaald naar een visuele oplossing met de naam Woodpecker.<\/p>\n<p>Eenvoudig gezegd bouwt Woodpecker een hoeveelheid kennis op uit de afbeelding en vervolgens kan een LLM die gebruiken als referentie om de oorspronkelijke beschrijving die door de MLLM is gegenereerd te corrigeren.<\/p>\n<p>Hier volgt een korte beschrijving van hoe het werkt:<\/p>\n<ol>\n<li>Een LLM zoals GPT-3.5 Turbo analyseert de beschrijving die door de MLLM wordt gegenereerd en extraheert sleutelconcepten zoals objecten, hoeveelheden en attributen. In de zin \"De man draagt een zwarte hoed.\" worden bijvoorbeeld de objecten \"man\" en \"hoed\" ge\u00ebxtraheerd.<\/li>\n<li>Een LLM wordt vervolgens gevraagd om vragen te genereren met betrekking tot deze concepten, zoals \"Staat er een man op de afbeelding?\" of \"Wat draagt de man?\".<\/li>\n<li>Deze vragen worden als prompts naar een Visual Question Answering (VQA) model gestuurd. De DINO voert objectdetectie en -telling uit, terwijl de BLIP-2-FlanT5 VQA attribuutgerelateerde vragen beantwoordt na analyse van het beeld.<\/li>\n<li>Een LLM combineert de antwoorden op de vragen tot een visuele kennisbank voor de afbeelding.<\/li>\n<li>Een LLM gebruikt deze referentie-informatie om eventuele hallucinaties in de oorspronkelijke beschrijving van de MLLM te corrigeren en details toe te voegen die de LLM over het hoofd heeft gezien.<\/li>\n<\/ol>\n<figure id=\"attachment_6780\" aria-describedby=\"caption-attachment-6780\" style=\"width: 1610px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-6780 size-full\" src=\"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/10\/Woodpecker-corrections.png\" alt=\"\" width=\"1610\" height=\"1010\" srcset=\"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/10\/Woodpecker-corrections.png 1610w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/10\/Woodpecker-corrections-300x188.png 300w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/10\/Woodpecker-corrections-1024x642.png 1024w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/10\/Woodpecker-corrections-768x482.png 768w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/10\/Woodpecker-corrections-1536x964.png 1536w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/10\/Woodpecker-corrections-370x232.png 370w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/10\/Woodpecker-corrections-800x502.png 800w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/10\/Woodpecker-corrections-20x13.png 20w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/10\/Woodpecker-corrections-740x464.png 740w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/10\/Woodpecker-corrections-1600x1004.png 1600w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/10\/Woodpecker-corrections-1320x828.png 1320w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/10\/Woodpecker-corrections-77x48.png 77w\" sizes=\"auto, (max-width: 1610px) 100vw, 1610px\" \/><figcaption id=\"caption-attachment-6780\" class=\"wp-caption-text\">Onjuiste beschrijvingen van MLLM samen met correcties van Woodpecker. Bron: <a href=\"https:\/\/arxiv.org\/pdf\/2310.16045.pdf\" target=\"_blank\" rel=\"noopener\">arXiv<\/a><\/figcaption><\/figure>\n<p>De onderzoekers gaven hun benadering de naam Woodpecker, een verwijzing naar de manier waarop de vogel insecten uit bomen plukt.<\/p>\n<p>Testresultaten toonden aan dat Woodpecker een nauwkeurigheidsverbetering behaalde van 30,66% voor MiniGPT4 en 24,33% voor de mPLUG-Owl modellen.<\/p>\n<p>Het generieke karakter van de modellen die voor deze aanpak nodig zijn, betekent dat de Woodpecker-aanpak gemakkelijk in verschillende MLLM's kan worden ge\u00efntegreerd.<\/p>\n<p>Als OpenAI Woodpecker in ChatGPT integreert, zouden we een duidelijke verbetering kunnen zien in de toch al indrukwekkende visuele prestaties. Een vermindering van MLLM hallucinatie zou ook geautomatiseerde besluitvorming kunnen verbeteren door systemen die visuele beschrijvingen als input gebruiken.<\/p>","protected":false},"excerpt":{"rendered":"<p>Multimodale Large Language Models (MLLM) zoals GPT-4V zijn erg goed in het analyseren en beschrijven van afbeeldingen, maar soms hallucineren ze en hebben ze het mis. Een nieuwe aanpak genaamd Woodpecker zou dat kunnen oplossen. Als je een MLLM vraagt om een foto te beschrijven, kan het normaal gesproken de objecten eruit pikken en de sc\u00e8ne nauwkeurig beschrijven. Maar net als bij antwoorden op tekstvragen doet het model soms aannames op basis van voorwerpen of concepten die vaak samen voorkomen. Daardoor kan een MLLM een foto van een winkelpui beschrijven en zeggen dat er mensen in de sc\u00e8ne zijn, terwijl die er in werkelijkheid niet zijn. repareren<\/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":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Woodpecker 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\/nl\/2023\/10\/woodpecker-could-solve-multimodal-llm-hallucinations\/\" \/>\n<meta property=\"og:locale\" content=\"nl_NL\" \/>\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. A new approach called Woodpecker could fix that. If you ask an MLLM to describe a photo it can normally pick out the objects and accurately describe the scene. But as with answers to text prompts, the model sometimes makes assumptions based on items or concepts that often appear together. As a result, an MLLM could describe a photo of a shopfront scene and say there are people in the scene when there actually aren\u2019t any. Fixing\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dailyai.com\/nl\/2023\/10\/woodpecker-could-solve-multimodal-llm-hallucinations\/\" \/>\n<meta property=\"og:site_name\" content=\"DailyAI\" \/>\n<meta property=\"article:published_time\" content=\"2023-10-26T09:17:15+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-10-26T09:22:02+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/10\/woodpecker.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1920\" \/>\n\t<meta property=\"og:image:height\" content=\"1080\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Eugene van der Watt\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DailyAIOfficial\" \/>\n<meta name=\"twitter:site\" content=\"@DailyAIOfficial\" \/>\n<meta name=\"twitter:label1\" content=\"Geschreven door\" \/>\n\t<meta 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