DAMA: Data- and Model-aware Alignment of Multi-modal LLMs
This addresses the challenge of improving trustworthiness and effectiveness in multi-modal LLMs for AI applications, representing an incremental advancement over existing methods.
The paper tackles the problem of imbalanced responsiveness in aligning multi-modal large language models with human preferences, proposing DAMA to dynamically adjust optimization based on data hardness and model responses, resulting in significant reductions in hallucination, such as 90.0% and 95.3% on Object-HalBench.
Direct Preference Optimization (DPO) has shown effectiveness in aligning multi-modal large language models (MLLM) with human preferences. However, existing methods exhibit an imbalanced responsiveness to the data of varying hardness, tending to overfit on the easy-to-distinguish data while underfitting on the hard-to-distinguish data. In this paper, we propose Data- and Model-aware DPO (DAMA) to dynamically adjust the optimization process from two key aspects: (1) a data-aware strategy that incorporates data hardness, and (2) a model-aware strategy that integrates real-time model responses. By combining the two strategies, DAMA enables the model to effectively adapt to data with varying levels of hardness. Extensive experiments on five benchmarks demonstrate that DAMA not only significantly enhances the trustworthiness, but also improves the effectiveness over general tasks. For instance, on the Object-HalBench, our DAMA-7B reduces response-level and mentioned-level hallucination by 90.0% and 95.3%, respectively, surpassing the performance of GPT-4V.