{"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\/nb\/2023\/10\/woodpecker-could-solve-multimodal-llm-hallucinations\/","title":{"rendered":"Woodpecker kan l\u00f8se multimodale LLM-hallusinasjoner"},"content":{"rendered":"

Multimodale store spr\u00e5kmodeller (MLLM) som GPT-4V er veldig gode til \u00e5 analysere og beskrive bilder, men noen ganger hallusinerer de og tar feil. En ny tiln\u00e6rming kalt Woodpecker kan fikse det.<\/strong><\/p>\n

Hvis du ber en MLLM om \u00e5 beskrive et bilde, kan den normalt plukke ut gjenstandene og gi en n\u00f8yaktig beskrivelse av motivet. Men i likhet med svar p\u00e5 tekstmeldinger gj\u00f8r modellen noen ganger antakelser basert p\u00e5 gjenstander eller begreper som ofte opptrer sammen.<\/p>\n

Resultatet er at en MLLM kan beskrive et bilde av en butikkfront og si at det er mennesker i bildet, selv om det faktisk ikke er noen.<\/p>\n

Det er et kontinuerlig arbeid \u00e5 fikse hallusinasjoner i tekstbaserte LLM-er, men det blir mye enklere n\u00e5r modellen er koblet til Internett. LLM-en kan generere et tekstsvar p\u00e5 en ledetekst, sjekke om det er sant basert p\u00e5 relevante internettdata, og korrigere seg selv der det er n\u00f8dvendig.<\/p>\n

Forskere fra Tencents YouTu Lab og University of Science and Technology of China tok denne tiln\u00e6rmingen og omsatte den til en visuell l\u00f8sning kalt Woodpecker.<\/p>\n

Enkelt sagt bygger Woodpecker opp en mengde kunnskap fra bildet, og deretter kan en LLM bruke den som referanse for \u00e5 korrigere den opprinnelige beskrivelsen som er generert av MLLM.<\/p>\n

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

    \n
  1. En LLM som GPT-3.5 Turbo analyserer beskrivelsen som genereres av MLLM, og trekker ut n\u00f8kkelbegreper som objekter, mengder og attributter. I setningen \"Mannen har p\u00e5 seg en svart hatt\" blir for eksempel objektene \"mann\" og \"hatt\" trukket ut.<\/li>\n
  2. En LLM blir deretter bedt om \u00e5 generere sp\u00f8rsm\u00e5l knyttet til disse begrepene, for eksempel \"Er det en mann p\u00e5 bildet?\" eller \"Hva har mannen p\u00e5 seg?\".<\/li>\n
  3. Disse sp\u00f8rsm\u00e5lene mates inn som sp\u00f8rsm\u00e5l til en VQA-modell (Visual Question Answering). Grounding DINO utf\u00f8rer objektdeteksjon og telling, mens BLIP-2-FlanT5 VQA besvarer attributtrelaterte sp\u00f8rsm\u00e5l etter \u00e5 ha analysert bildet.<\/li>\n
  4. En LLM kombinerer svarene p\u00e5 sp\u00f8rsm\u00e5lene til en visuell kunnskapsbase for bildet.<\/li>\n
  5. En LLM bruker denne referansekunnskapen til \u00e5 korrigere eventuelle hallusinasjoner i den opprinnelige MLLM-beskrivelsen, og legger til detaljer den har oversett.<\/li>\n<\/ol>\n
    \"\"
    Feilaktige beskrivelser fra MLLM sammen med rettelser fra Woodpecker. Kilde: arXiv<\/a><\/figcaption><\/figure>\n

    Forskerne kalte metoden Woodpecker (hakkespett) med referanse til hvordan fuglen plukker insekter ut av tr\u00e6rne.<\/p>\n

    Testresultatene viste at Woodpecker oppn\u00e5dde en n\u00f8yaktighetsforbedring p\u00e5 30,66% for MiniGPT4 og 24,33% for mPLUG-Owl-modellene.<\/p>\n

    De generiske modellene som kreves i denne tiln\u00e6rmingen, gj\u00f8r at Woodpecker-tiln\u00e6rmingen enkelt kan integreres i ulike MLLM-er.<\/p>\n

    Hvis OpenAI integrerer Woodpecker i ChatGPT, kan vi f\u00e5 se en markant forbedring av den allerede imponerende visuelle ytelsen. En reduksjon i MLLM-hallusinasjon kan ogs\u00e5 forbedre automatisert beslutningstaking i systemer som bruker visuelle beskrivelser som inndata.<\/p>","protected":false},"excerpt":{"rendered":"

    Multimodale store spr\u00e5kmodeller (MLLM) som GPT-4V er veldig gode til \u00e5 analysere og beskrive bilder, men noen ganger hallusinerer de og tar feil. En ny tiln\u00e6rming kalt Woodpecker kan l\u00f8se dette problemet. Hvis du ber en MLLM om \u00e5 beskrive et bilde, kan den normalt plukke ut objektene og beskrive scenen n\u00f8yaktig. Men i likhet med svar p\u00e5 tekstmeldinger gj\u00f8r modellen noen ganger antakelser basert p\u00e5 gjenstander eller begreper som ofte opptrer sammen. Derfor kan en MLLM beskrive et bilde av en butikkfront og si at det er mennesker i bildet, selv om det faktisk ikke er noen. Fiksering<\/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\/nb\/2023\/10\/woodpecker-could-solve-multimodal-llm-hallucinations\/\" \/>\n<meta property=\"og:locale\" content=\"nb_NO\" \/>\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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