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 excel in generating responses based on visual inputs. However%2C they often suffer from a bias towards generating responses similar to their pretraining corpus%2C overshadowing the importance of visual information. We treat this bias as a preference for pretraining statistics%2C which hinders the model's grounding in visual input. To mitigate this issue%2C we propose Bootstrapped Preference Optimization (BPO)%2C which conducts preference learning with datasets containing negative responses bootstrapped from the model itself. Specifically%2C we propose the following two strategies: 1) using distorted image inputs to the MLLM for eliciting responses that contain signified pretraining bias%3B 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%2C which is subsequently utilized to perform preference learning. Our approach effectively suppresses pretrained LLM bias%2C enabling enhanced grounding in visual inputs. Extensive experimentation demonstrates significant performance improvements across multiple benchmarks%2C advancing the state-of-the-art in multimodal conversational systems.?quality=80&w=800)
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