{"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\/pt\/2023\/10\/woodpecker-could-solve-multimodal-llm-hallucinations\/","title":{"rendered":"O pica-pau pode resolver as alucina\u00e7\u00f5es multimodais do LLM"},"content":{"rendered":"

Os Modelos Multimodais de Linguagem Ampla (MLLM), como o GPT-4V, s\u00e3o muito bons a analisar e a descrever imagens, mas por vezes t\u00eam alucina\u00e7\u00f5es e enganam-se. Uma nova abordagem chamada Woodpecker pode resolver isso.<\/strong><\/p>\n

Se pedirmos a um MLLM para descrever uma fotografia, ele consegue normalmente identificar os objectos e descrever a cena com precis\u00e3o. Mas, tal como acontece com as respostas a pedidos de texto, o modelo faz por vezes suposi\u00e7\u00f5es baseadas em objectos ou conceitos que aparecem frequentemente juntos.<\/p>\n

Consequentemente, um MLLM pode descrever uma fotografia de uma fachada de uma loja e dizer que h\u00e1 pessoas na cena, quando na realidade n\u00e3o h\u00e1 nenhumas.<\/p>\n

A corre\u00e7\u00e3o de alucina\u00e7\u00f5es em LLMs baseados em texto est\u00e1 em curso, mas torna-se muito mais f\u00e1cil quando o modelo est\u00e1 ligado \u00e0 Internet. O LLM pode gerar uma resposta de texto a um pedido, verificar a sua veracidade com base em dados relevantes da Internet e auto-corrigir-se quando necess\u00e1rio.<\/p>\n

Os cientistas do Laborat\u00f3rio YouTu da Tencent e da Universidade de Ci\u00eancia e Tecnologia da China adoptaram esta abordagem e traduziram-na numa solu\u00e7\u00e3o visual chamada Woodpecker.<\/p>\n

Em termos simples, o Woodpecker constr\u00f3i um conjunto de conhecimentos a partir da imagem e, em seguida, um LLM pode utiliz\u00e1-lo como refer\u00eancia para corrigir a descri\u00e7\u00e3o inicial gerada pelo MLLM.<\/p>\n

Segue-se uma breve descri\u00e7\u00e3o do seu funcionamento:<\/p>\n

    \n
  1. Um LLM como o GPT-3.5 Turbo analisa a descri\u00e7\u00e3o gerada pelo MLLM e extrai conceitos-chave como objectos, quantidades e atributos. Por exemplo, na frase \"O homem est\u00e1 a usar um chap\u00e9u preto\", s\u00e3o extra\u00eddos os objectos \"homem\" e \"chap\u00e9u\".<\/li>\n
  2. Um LLM \u00e9 ent\u00e3o levado a gerar perguntas relacionadas com estes conceitos, como \"H\u00e1 um homem na imagem?\" ou \"O que \u00e9 que o homem tem vestido?\".<\/li>\n
  3. Estas perguntas s\u00e3o transmitidas como instru\u00e7\u00f5es a um modelo de resposta a perguntas visuais (VQA). O Grounding DINO efectua a dete\u00e7\u00e3o e contagem de objectos, enquanto o BLIP-2-FlanT5 VQA responde a perguntas relacionadas com atributos depois de analisar a imagem.<\/li>\n
  4. Um LLM combina as respostas \u00e0s perguntas numa base de conhecimento visual para a imagem.<\/li>\n
  5. Um LLM utiliza este corpo de conhecimentos de refer\u00eancia para corrigir quaisquer alucina\u00e7\u00f5es na descri\u00e7\u00e3o original do MLLM e acrescenta pormenores que lhe escaparam.<\/li>\n<\/ol>\n
    \"\"
    Descri\u00e7\u00f5es incorrectas de MLLM e correc\u00e7\u00f5es de Woodpecker. Fonte: arXiv<\/a><\/figcaption><\/figure>\n

    Os investigadores deram \u00e0 sua abordagem o nome de Pica-pau, em refer\u00eancia \u00e0 forma como a ave apanha insectos das \u00e1rvores.<\/p>\n

    Os resultados dos testes mostraram que o Woodpecker alcan\u00e7ou uma melhoria de precis\u00e3o de 30,66% para o MiniGPT4 e 24,33% para os modelos mPLUG-Owl.<\/p>\n

    O car\u00e1cter gen\u00e9rico dos modelos exigidos nesta abordagem significa que a abordagem Woodpecker pode ser facilmente integrada em v\u00e1rios MLLM.<\/p>\n

    Se a OpenAI integrar o Woodpecker no ChatGPT, poderemos assistir a uma melhoria significativa do j\u00e1 impressionante desempenho visual. Uma redu\u00e7\u00e3o da alucina\u00e7\u00e3o MLLM poderia tamb\u00e9m melhorar a tomada de decis\u00f5es automatizada por parte dos sistemas que utilizam descri\u00e7\u00f5es visuais como inputs.<\/p>","protected":false},"excerpt":{"rendered":"

    Os Modelos Multimodais de Linguagem Ampla (MLLM), como o GPT-4V, s\u00e3o muito bons a analisar e a descrever imagens, mas por vezes t\u00eam alucina\u00e7\u00f5es e enganam-se. Uma nova abordagem chamada Woodpecker pode resolver esse problema. Se pedirmos a um MLLM para descrever uma fotografia, normalmente ele consegue identificar os objectos e descrever a cena com precis\u00e3o. Mas, tal como acontece com as respostas a pedidos de texto, o modelo faz por vezes suposi\u00e7\u00f5es com base em objectos ou conceitos que aparecem frequentemente juntos. Como resultado, um MLLM pode descrever uma fotografia de uma loja e dizer que h\u00e1 pessoas na cena, quando na realidade n\u00e3o h\u00e1. Fixa\u00e7\u00e3o<\/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\/pt\/2023\/10\/woodpecker-could-solve-multimodal-llm-hallucinations\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\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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