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+excel+in+generating+responses+based+on+visual+inputs.+However%252C+they+often+suffer+from+a+bias+towards+generating+responses+similar+to+their+pretraining+corpus%252C+overshadowing+the+importance+of+visual+information.+We+treat+this+bias+as+a+preference+for+pretraining+statistics%252C+which+hinders+the+model%27s+grounding+in+visual+input.+To+mitigate+this+issue%252C+we+propose+Bootstrapped+Preference+Optimization+(BPO)%252C+which+conducts+preference+learning+with+datasets+containing+negative+responses+bootstrapped+from+the+model+itself.+Specifically%252C+we+propose+the+following+two+strategies%3a+1)+using+distorted+image+inputs+to+the+MLLM+for+eliciting+responses+that+contain+signified+pretraining+bias%253B+2)+leveraging+text-based+LLM+to+explicitly+inject+erroneous+but+common+elements+into+the+original+response.+Those+undesirable+responses+are+paired+with+original+annotated+responses+from+the+datasets+to+construct+the+preference+dataset%252C+which+is+subsequently+utilized+to+perform+preference+learning.+Our+approach+effectively+suppresses+pretrained+LLM+bias%252C+enabling+enhanced+grounding+in+visual+inputs.+Extensive+experimentation+demonstrates+significant+performance+improvements+across+multiple+benchmarks%252C+advancing+the+state-of-the-art+in+multimodal+conversational+systems.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.b97ae45593c773000a783d93c7e9be60%3frik%3dmR93ujdXCbOaGg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Multimodal+Language+Model+Database&FORM=IRPRST&ck=B94C770EB3A8A143AFCF392F87B764FA&selectedIndex=6&itb=0)
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