PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging
Multimodal Large Language Models (MLLMs) have become increasingly significant in the field of artificial intelligence, as they are designed to process and understand information from multiple modalities, such as text, images, and sound. Recently, a groundbreaking study titled “PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging” has been released on arXiv, offering a novel approach to integrating these models.
The study highlights the reliance of MLLMs on multimodal pre-training over diverse datasets, where different datasets impart complementary cross-modal alignment capabilities. While the existing model merging research has primarily focused on post-finetuning scenarios, this study emphasizes the largely unexplored pre-training stage. The authors argue that the core of MLLM pre-training is the establishment of effective cross-modal alignment, which merges visual and textual representations into a unified semantic space.
Challenges in Multimodal Pre-training
The authors introduce the concept of post-alignment merging, which aims to consolidate cross-modal alignment capabilities learned from heterogeneous multimodal pre-training. This approach introduces two significant challenges:
- Cross-domain parameter interference: Parameter updates derived from different data distributions may conflict during the merging process, hindering the model’s ability to learn effectively.
- Layer-wise alignment contribution disparity: Different layers and projectors may contribute unevenly to the overall cross-modal alignment, complicating the merging process.
The PivotMerge Framework
To address these challenges, the authors propose PivotMerge, a cutting-edge post-alignment merging framework specifically designed for cross-modal projectors. PivotMerge encompasses two core components:
- Shared-space Decomposition and Filtering: This component disentangles shared alignment patterns from domain-specific variations, effectively suppressing conflicting directions that could disrupt the merging process.
- Alignment-guided Layer-wise Merging: This mechanism assigns layer-specific merging weights based on the varying alignment contributions of each layer, ensuring a more balanced integration of knowledge.
Evaluation and Results
The researchers constructed systematic CC12M-based post-alignment merging scenarios for thorough evaluation of PivotMerge. Extensive experiments were conducted across multiple multimodal benchmarks, revealing that PivotMerge consistently outperforms existing baselines. The results demonstrate not only the effectiveness of the framework but also its ability to generalize across different tasks and datasets.
Conclusion
PivotMerge represents a significant advancement in the realm of multimodal pre-training, providing a robust framework for integrating diverse MLLMs. By addressing the challenges of cross-domain parameter interference and layer-wise alignment contribution disparity, PivotMerge paves the way for more effective model merging strategies. As the field of artificial intelligence continues to evolve, innovations such as PivotMerge will play a crucial role in enhancing the capabilities of multimodal models, driving further advancements in understanding and processing complex information.
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