{"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\/fr\/2023\/10\/woodpecker-could-solve-multimodal-llm-hallucinations\/","title":{"rendered":"Le pic pourrait r\u00e9soudre les hallucinations multimodales du LLM"},"content":{"rendered":"

Les mod\u00e8les multimodaux \u00e0 grand langage (MLLM) tels que GPT-4V sont tr\u00e8s efficaces pour analyser et d\u00e9crire les images, mais il leur arrive parfois d'halluciner et de se tromper. Une nouvelle approche appel\u00e9e Woodpecker pourrait y rem\u00e9dier.<\/strong><\/p>\n

Si vous demandez \u00e0 un MLLM de d\u00e9crire une photo, il peut normalement identifier les objets et d\u00e9crire la sc\u00e8ne avec pr\u00e9cision. Mais comme pour les r\u00e9ponses \u00e0 des textes, le mod\u00e8le \u00e9met parfois des hypoth\u00e8ses bas\u00e9es sur des \u00e9l\u00e9ments ou des concepts qui apparaissent souvent ensemble.<\/p>\n

Par cons\u00e9quent, un MLLM peut d\u00e9crire une photo de devanture de magasin et dire qu'il y a des gens dans la sc\u00e8ne alors qu'il n'y en a pas.<\/p>\n

La correction des hallucinations dans les LLM \u00e0 base de texte est en cours mais devient beaucoup plus facile lorsque le mod\u00e8le est connect\u00e9 \u00e0 l'internet. Le LLM peut g\u00e9n\u00e9rer une r\u00e9ponse textuelle \u00e0 une invite, en v\u00e9rifier la v\u00e9racit\u00e9 sur la base de donn\u00e9es Internet pertinentes et s'auto-corriger si n\u00e9cessaire.<\/p>\n

Des scientifiques du laboratoire YouTu de Tencent et de l'Universit\u00e9 des sciences et technologies de Chine ont adopt\u00e9 cette approche et l'ont traduite en une solution visuelle appel\u00e9e Woodpecker.<\/p>\n

En termes simples, Woodpecker construit un ensemble de connaissances \u00e0 partir de l'image et un LLM peut ensuite l'utiliser comme r\u00e9f\u00e9rence pour corriger la description initiale g\u00e9n\u00e9r\u00e9e par le MLLM.<\/p>\n

Voici une br\u00e8ve description de son fonctionnement :<\/p>\n

    \n
  1. Un LLM tel que GPT-3.5 Turbo analyse la description g\u00e9n\u00e9r\u00e9e par le MLLM et extrait les concepts cl\u00e9s tels que les objets, les quantit\u00e9s et les attributs. Par exemple, dans la phrase \"L'homme porte un chapeau noir\", les objets \"homme\" et \"chapeau\" sont extraits.<\/li>\n
  2. Un LLM est ensuite invit\u00e9 \u00e0 g\u00e9n\u00e9rer des questions li\u00e9es \u00e0 ces concepts, telles que \"Y a-t-il un homme sur l'image ?\" ou \"Que porte l'homme ?\".<\/li>\n
  3. Ces questions sont transmises \u00e0 un mod\u00e8le de r\u00e9ponse aux questions visuelles (VQA). La DINO de mise \u00e0 la terre effectue la d\u00e9tection et le comptage des objets, tandis que le VQA BLIP-2-FlanT5 r\u00e9pond aux questions li\u00e9es aux attributs apr\u00e8s avoir analys\u00e9 l'image.<\/li>\n
  4. Un LLM combine les r\u00e9ponses aux questions dans une base de connaissances visuelles pour l'image.<\/li>\n
  5. Le LLM utilise ce corpus de r\u00e9f\u00e9rence pour corriger les \u00e9ventuelles hallucinations de la description originale du MLLM et ajouter les d\u00e9tails qui lui ont \u00e9chapp\u00e9.<\/li>\n<\/ol>\n
    \"\"
    Descriptions erron\u00e9es de MLLM et corrections de Woodpecker. Source : arXiv<\/a><\/figcaption><\/figure>\n

    Les chercheurs ont baptis\u00e9 leur approche \"Pic\" en r\u00e9f\u00e9rence \u00e0 la fa\u00e7on dont l'oiseau ramasse les insectes dans les arbres.<\/p>\n

    Les r\u00e9sultats des tests ont montr\u00e9 que Woodpecker a am\u00e9lior\u00e9 la pr\u00e9cision de 30,66% pour MiniGPT4 et de 24,33% pour les mod\u00e8les mPLUG-Owl.<\/p>\n

    La nature g\u00e9n\u00e9rique des mod\u00e8les requis dans cette approche signifie que l'approche Woodpecker pourrait facilement \u00eatre int\u00e9gr\u00e9e dans divers MLLM.<\/p>\n

    Si OpenAI int\u00e8gre Woodpecker dans ChatGPT, nous pourrions voir une nette am\u00e9lioration des performances visuelles d\u00e9j\u00e0 impressionnantes. Une r\u00e9duction de l'hallucination MLLM pourrait \u00e9galement am\u00e9liorer la prise de d\u00e9cision automatis\u00e9e par les syst\u00e8mes qui utilisent des descriptions visuelles comme donn\u00e9es d'entr\u00e9e.<\/p>","protected":false},"excerpt":{"rendered":"

    Les mod\u00e8les multimodaux \u00e0 grand langage (MLLM) tels que GPT-4V sont tr\u00e8s efficaces pour analyser et d\u00e9crire les images, mais il leur arrive parfois d'halluciner et de se tromper. Une nouvelle approche appel\u00e9e Woodpecker pourrait y rem\u00e9dier. Si vous demandez \u00e0 un MLLM de d\u00e9crire une photo, il peut normalement rep\u00e9rer les objets et d\u00e9crire la sc\u00e8ne avec pr\u00e9cision. Mais comme pour les r\u00e9ponses \u00e0 des textes, le mod\u00e8le \u00e9met parfois des hypoth\u00e8ses bas\u00e9es sur des \u00e9l\u00e9ments ou des concepts qui apparaissent souvent ensemble. Ainsi, un MLLM peut d\u00e9crire une photo de devanture de magasin et dire qu'il y a des personnes dans la sc\u00e8ne alors qu'il n'y en a pas. Fixation<\/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\/fr\/2023\/10\/woodpecker-could-solve-multimodal-llm-hallucinations\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\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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