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%252C+and+Large+Language+Model+(LLM)+analysis+to+comprehensively+understand+and+classify+harmful+memes.+Utilizing+the+BLIP+model+for+image+captioning%252C+PP-OCR+and+TrOCR+for+text+recognition+across+multiple+languages%252C+and+the+Qwen+LLM+for+nuanced+language+understanding%252C+our+system+is+capable+of+identifying+harmful+content+in+memes+created+in+English%252C+Chinese%252C+Malay%252C+and+Tamil.+To+enhance+the+system%27s+performance%252C+we+fine-tuned+our+approach+by+leveraging+additional+data+labeled+using+GPT-4V%252C+aiming+to+distill+the+understanding+capability+of+GPT-4V+for+harmful+memes+to+our+system.+Our+framework+achieves+top-1+at+the+public+leaderboard+of+the+Online+Safety+Prize+Challenge+hosted+by+AI+Singapore%252C+with+the+AUROC+as+0.7749+and+accuracy+as+0.7087%252C+significantly+ahead+of+the+other+teams.+Notably%252C+our+approach+outperforms+previous+benchmarks%252C+with+FLAVA+achieving+an+AUROC+of+0.5695+and+VisualBERT+an+AUROC+of+0.5561.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.2d1fde47d92a4b49ec4b70c8a87c3de7%3frik%3dRyN0D57vT83yeA%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Large+Language+Model+Memes&FORM=IRPRST&ck=B4C28610FD2C9306CAAA6FAA2A0601A3&selectedIndex=7&itb=0)
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