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+have+demonstrated+remarkable+reasoning+capabilities%252C+notably+in+connecting+ideas+and+adhering+to+logical+rules+to+solve+problems.+These+models+have+evolved+to+accommodate+various+data+modalities%252C+including+sound+and+images%252C+known+as+multimodal+LLMs+(MLLMs)%252C+which+are+capable+of+describing+images+or+sound+recordings.+Previous+work+has+demonstrated+that+when+the+LLM+component+in+MLLMs+is+frozen%252C+the+audio+or+visual+encoder+serves+to+caption+the+sound+or+image+input+facilitating+text-based+reasoning+with+the+LLM+component.+We+are+interested+in+using+the+LLM%27s+reasoning+capabilities+in+order+to+facilitate+classification.+In+this+paper%252C+we+demonstrate+through+a+captioning%252Fclassification+experiment+that+an+audio+MLLM+cannot+fully+leverage+its+LLM%27s+text-based+reasoning+when+generating+audio+captions.+We+also+consider+how+this+may+be+due+to+MLLMs+separately+representing+auditory+and+textual+information+such+that+it+severs+the+reasoning+pathway+from+the+LLM+to+the+audio+encoder.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.18106b612114e87a0c43b083ca06aafe%3frik%3dHFQoZ3RRuNqNtQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Mllm+Task&FORM=IRPRST&ck=BBA563A5B40EE62A833C1D42BFD32C58&selectedIndex=26&itb=0)
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