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%252C+datasets+used+for+visual+question+answering+(VQA)+and+referring+expression+comprehension+have+seen+a+resurgence.+However%252C+the+most+popular+datasets+used+to+evaluate+MLLMs+are+some+of+the+earliest+ones+created%252C+and+they+have+many+known+problems%252C+including+extreme+bias%252C+spurious+correlations%252C+and+an+inability+to+permit+fine-grained+analysis.+In+this+paper%252C+we+pioneer+evaluating+recent+MLLMs+(LLaVA+1.5%252C+LLaVA-NeXT%252C+BLIP2%252C+InstructBLIP%252C+GPT-4V%252C+and+GPT-4o)+on+datasets+designed+to+address+weaknesses+in+earlier+ones.+We+assess+three+VQA+datasets%3a+1)+TDIUC%252C+which+permits+fine-grained+analysis+on+12+question+types%253B+2)+TallyQA%252C+which+has+simple+and+complex+counting+questions%253B+and+3)+DVQA%252C+which+requires+optical+character+recognition+for+chart+understanding.+We+also+study+VQDv1%252C+a+dataset+that+requires+identifying+all+image+regions+that+satisfy+a+given+query.+Our+experiments+reveal+the+weaknesses+of+many+MLLMs+that+have+not+previously+been+reported.+Our+code+is+integrated+into+the+widely+used+LAVIS+framework+for+MLLM+evaluation%252C+enabling+the+rapid+assessment+of+future+MLLMs.+Project+webpage%3a+https%3a%252F%252Fkevinlujian.github.io%252FMLLM_Evaluations%252F&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.7c5ea1321f04b4f1742014249c28c748%3frik%3daIuSYV5xMtUGgA%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=LLM+Multimodality&simid=608031894048492869&FORM=IRPRST&ck=9E9C9AF5CED20A16630ACB4FB75B3C1E&selectedIndex=10&itb=0)
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