DA-DPO: Cost-efficient Difficulty-aware Preference Optimization for Reducing MLLM Hallucinations

This work presents DA-DPO, a cost-efficient and difficulty-aware preference optimization method aimed at significantly reducing hallucinations in Multimodal Large Language Models (MLLMs). By optimizing based on content difficulty, the approach improves the factual consistency and reliability of MLLM outputs.