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+have+undergone+substantial+advancements%252C+augmenting+off-the-shelf+LLMs+to+support+MM+inputs+or+outputs+via+cost-effective+training+strategies.+The+resulting+models+not+only+preserve+the+inherent+reasoning+and+decision-making+capabilities+of+LLMs+but+also+empower+a+diverse+range+of+MM+tasks.+In+this+paper%252C+we+provide+a+comprehensive+survey+aimed+at+facilitating+further+research+of+MM-LLMs.+Initially%252C+we+outline+general+design+formulations+for+model+architecture+and+training+pipeline.+Subsequently%252C+we+introduce+a+taxonomy+encompassing+126+MM-LLMs%252C+each+characterized+by+its+specific+formulations.+Furthermore%252C+we+review+the+performance+of+selected+MM-LLMs+on+mainstream+benchmarks+and+summarize+key+training+recipes+to+enhance+the+potency+of+MM-LLMs.+Finally%252C+we+explore+promising+directions+for+MM-LLMs+while+concurrently+maintaining+a+real-time+tracking+website+for+the+latest+developments+in+the+field.+We+hope+that+this+survey+contributes+to+the+ongoing+advancement+of+the+MM-LLMs+domain.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.e50c9ffe6158ece16ed724263f8eed8b%3frik%3dJSMGfwcDQbY%252b8A%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=LMM+Large+Multimodal+Model&FORM=IRPRST&ck=298F76F782103DCAD8DBC87367160B4F&selectedIndex=26&itb=0)
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