{"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\/sv\/2023\/10\/woodpecker-could-solve-multimodal-llm-hallucinations\/","title":{"rendered":"Hackspett kan l\u00f6sa multimodala LLM-hallucinationer"},"content":{"rendered":"

Multimodala stora spr\u00e5kmodeller (MLLM) som GPT-4V \u00e4r riktigt bra p\u00e5 att analysera och beskriva bilder, men ibland hallucinerar de och f\u00e5r saker om bakfoten. Ett nytt tillv\u00e4gag\u00e5ngss\u00e4tt som kallas Woodpecker kan \u00e5tg\u00e4rda det.<\/strong><\/p>\n

Om du ber en MLLM att beskriva ett foto kan den normalt plocka ut f\u00f6rem\u00e5len och beskriva scenen p\u00e5 ett korrekt s\u00e4tt. Men precis som med svar p\u00e5 textfr\u00e5gor g\u00f6r modellen ibland antaganden baserade p\u00e5 f\u00f6rem\u00e5l eller begrepp som ofta f\u00f6rekommer tillsammans.<\/p>\n

Det inneb\u00e4r att en MLLM kan beskriva ett foto av en butiksfasad och s\u00e4ga att det finns m\u00e4nniskor i bilden, trots att det inte finns n\u00e5gra.<\/p>\n

Att \u00e5tg\u00e4rda hallucinationer i textbaserade LLM:er \u00e4r ett st\u00e4ndigt p\u00e5g\u00e5ende arbete, men det blir mycket enklare n\u00e4r modellen \u00e4r ansluten till internet. LLM kan generera ett textsvar p\u00e5 en uppmaning, kontrollera att det \u00e4r korrekt baserat p\u00e5 relevanta internetdata och sj\u00e4lvkorrigera vid behov.<\/p>\n

Forskare fr\u00e5n Tencents YouTu Lab och University of Science and Technology of China tog fasta p\u00e5 detta syns\u00e4tt och \u00f6versatte det till en visuell l\u00f6sning som kallas Woodpecker.<\/p>\n

Enkelt uttryckt bygger Woodpecker upp en kunskapsmassa fr\u00e5n bilden och sedan kan en LLM anv\u00e4nda den som referens f\u00f6r att korrigera den ursprungliga beskrivningen som genererats av MLLM.<\/p>\n

H\u00e4r f\u00f6ljer en kort beskrivning av hur det fungerar:<\/p>\n

    \n
  1. En LLM som GPT-3.5 Turbo analyserar den beskrivning som genereras av MLLM och extraherar nyckelbegrepp som objekt, kvantiteter och attribut. I meningen \"Mannen b\u00e4r en svart hatt.\" extraheras till exempel objekten \"man\" och \"hatt\".<\/li>\n
  2. En LLM uppmanas sedan att generera fr\u00e5gor relaterade till dessa begrepp, t.ex. \"Finns det en man p\u00e5 bilden?\" eller \"Vad har mannen p\u00e5 sig?\".<\/li>\n
  3. Dessa fr\u00e5gor matas som uppmaningar till en VQA-modell (Visual Question Answering). Grounding DINO utf\u00f6r objektdetektering och r\u00e4kning medan BLIP-2-FlanT5 VQA svarar p\u00e5 attributrelaterade fr\u00e5gor efter att ha analyserat bilden.<\/li>\n
  4. En LLM kombinerar svaren p\u00e5 fr\u00e5gorna till en visuell kunskapsbas f\u00f6r bilden.<\/li>\n
  5. En LLM anv\u00e4nder denna referenskunskap f\u00f6r att korrigera eventuella hallucinationer i den ursprungliga MLLM:s beskrivning och l\u00e4gger till detaljer som den missade.<\/li>\n<\/ol>\n
    \"\"
    Felaktiga beskrivningar fr\u00e5n MLLM tillsammans med korrigeringar fr\u00e5n Woodpecker. K\u00e4llor: arXiv<\/a><\/figcaption><\/figure>\n

    Forskarna d\u00f6pte sin metod till Woodpecker med h\u00e4nvisning till hur f\u00e5geln plockar insekter ur tr\u00e4den.<\/p>\n

    Testresultaten visade att Woodpecker uppn\u00e5dde en f\u00f6rb\u00e4ttring av noggrannheten med 30,66% f\u00f6r MiniGPT4 och 24,33% f\u00f6r mPLUG-Owl-modellerna.<\/p>\n

    Den generiska karakt\u00e4ren hos de modeller som kr\u00e4vs i denna metod inneb\u00e4r att Woodpecker-metoden enkelt kan integreras i olika MLLM.<\/p>\n

    Om OpenAI integrerar Woodpecker i ChatGPT kan vi f\u00e5 se en markant f\u00f6rb\u00e4ttring av den redan imponerande visuella prestandan. En minskning av MLLM-hallucinationer skulle ocks\u00e5 kunna f\u00f6rb\u00e4ttra automatiserat beslutsfattande i system som anv\u00e4nder visuella beskrivningar som indata.<\/p>","protected":false},"excerpt":{"rendered":"

    Multimodala stora spr\u00e5kmodeller (MLLM) som GPT-4V \u00e4r riktigt bra p\u00e5 att analysera och beskriva bilder, men ibland hallucinerar de och f\u00e5r saker om bakfoten. Ett nytt tillv\u00e4gag\u00e5ngss\u00e4tt som kallas Woodpecker kan fixa det. Om du ber en MLLM att beskriva ett foto kan den normalt plocka ut objekten och beskriva scenen korrekt. Men precis som n\u00e4r det g\u00e4ller svar p\u00e5 textfr\u00e5gor g\u00f6r modellen ibland antaganden baserade p\u00e5 f\u00f6rem\u00e5l eller begrepp som ofta f\u00f6rekommer tillsammans. D\u00e4rf\u00f6r kan en MLLM beskriva ett foto av ett skyltf\u00f6nster och s\u00e4ga att det finns m\u00e4nniskor i bilden n\u00e4r det i sj\u00e4lva verket inte finns n\u00e5gra. Fixering<\/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\/sv\/2023\/10\/woodpecker-could-solve-multimodal-llm-hallucinations\/\" \/>\n<meta property=\"og:locale\" content=\"sv_SE\" \/>\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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