{"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\/da\/2023\/10\/woodpecker-could-solve-multimodal-llm-hallucinations\/","title":{"rendered":"Sp\u00e6tte kan l\u00f8se multimodale LLM-hallucinationer"},"content":{"rendered":"

Multimodale store sprogmodeller (MLLM) som GPT-4V er virkelig gode til at analysere og beskrive billeder, men nogle gange hallucinerer de og tager fejl. En ny tilgang kaldet Woodpecker kan rette op p\u00e5 det.<\/strong><\/p>\n

Hvis du beder en MLLM om at beskrive et foto, kan den normalt udpege objekterne og beskrive scenen n\u00f8jagtigt. Men ligesom med svar p\u00e5 tekstprompter g\u00f8r modellen nogle gange antagelser baseret p\u00e5 genstande eller begreber, der ofte optr\u00e6der sammen.<\/p>\n

Det betyder, at en MLLM kan beskrive et foto af en butiksfacade og sige, at der er mennesker i billedet, selvom der faktisk ikke er nogen.<\/p>\n

Det er et l\u00f8bende arbejde at rette hallucinationer i tekstbaserede LLM'er, men det bliver meget nemmere, n\u00e5r modellen er forbundet til internettet. LLM'en kan generere et tekstsvar p\u00e5 en opfordring, tjekke det for \u00e6gthed baseret p\u00e5 relevante internetdata og selv korrigere, hvor det er n\u00f8dvendigt.<\/p>\n

Forskere fra Tencents YouTu Lab og University of Science and Technology of China tog denne tilgang og omsatte den til en visuel l\u00f8sning kaldet Woodpecker.<\/p>\n

Enkelt sagt opbygger Woodpecker en m\u00e6ngde viden ud fra billedet, og s\u00e5 kan en LLM bruge det som reference til at korrigere den oprindelige beskrivelse, der er genereret af MLLM.<\/p>\n

Her er en kort beskrivelse af, hvordan det fungerer:<\/p>\n

    \n
  1. En LLM som GPT-3.5 Turbo analyserer den beskrivelse, der genereres af MLLM, og udtr\u00e6kker n\u00f8glebegreber som objekter, m\u00e6ngder og attributter. I s\u00e6tningen \"Manden har en sort hat p\u00e5.\" udtr\u00e6kkes f.eks. objekterne \"mand\" og \"hat\".<\/li>\n
  2. En LLM bliver derefter bedt om at generere sp\u00f8rgsm\u00e5l relateret til disse begreber som \"Er der en mand p\u00e5 billedet?\" eller \"Hvad har manden p\u00e5?\".<\/li>\n
  3. Disse sp\u00f8rgsm\u00e5l sendes som prompts til en VQA-model (Visual Question Answering). Grounding DINO udf\u00f8rer objektdetektering og -t\u00e6lling, mens BLIP-2-FlanT5 VQA besvarer attributrelaterede sp\u00f8rgsm\u00e5l efter at have analyseret billedet.<\/li>\n
  4. En LLM kombinerer svarene p\u00e5 sp\u00f8rgsm\u00e5lene til en visuel vidensbase for billedet.<\/li>\n
  5. En LLM bruger denne referenceviden til at korrigere eventuelle hallucinationer i den oprindelige MLLM's beskrivelse og tilf\u00f8jer detaljer, som den har overset.<\/li>\n<\/ol>\n
    \"\"
    Forkerte beskrivelser fra MLLM sammen med rettelser fra Woodpecker. Kilde: arXiv<\/a><\/figcaption><\/figure>\n

    Forskerne kaldte deres metode Woodpecker med henvisning til, hvordan fuglen plukker insekter ud af tr\u00e6erne.<\/p>\n

    Testresultaterne viste, at Woodpecker opn\u00e5ede en forbedring af n\u00f8jagtigheden p\u00e5 30,66% for MiniGPT4 og 24,33% for mPLUG-Owl-modellerne.<\/p>\n

    Den generiske karakter af de modeller, der kr\u00e6ves i denne tilgang, betyder, at Woodpecker-tilgangen nemt kan integreres i forskellige MLLM'er.<\/p>\n

    Hvis OpenAI integrerer Woodpecker i ChatGPT, kan vi se en markant forbedring af den allerede imponerende visuelle ydeevne. En reduktion af MLLM-hallucinationer kan ogs\u00e5 forbedre automatiseret beslutningstagning i systemer, der bruger visuelle beskrivelser som input.<\/p>","protected":false},"excerpt":{"rendered":"

    Multimodale store sprogmodeller (MLLM) som GPT-4V er virkelig gode til at analysere og beskrive billeder, men nogle gange hallucinerer de og tager fejl. Det kan en ny tilgang kaldet Woodpecker g\u00f8re noget ved. Hvis du beder en MLLM om at beskrive et foto, kan den normalt udpege objekterne og beskrive scenen n\u00f8jagtigt. Men ligesom med svar p\u00e5 tekstprompter g\u00f8r modellen nogle gange antagelser baseret p\u00e5 genstande eller begreber, der ofte optr\u00e6der sammen. Derfor kan en MLLM beskrive et foto af en butiksfacade og sige, at der er mennesker i scenen, selvom der faktisk ikke er nogen. Fasts\u00e6ttelse<\/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\/da\/2023\/10\/woodpecker-could-solve-multimodal-llm-hallucinations\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\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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