AsymLoRA: Harmonizing Data Conflicts and Commonalities in MLLMs

• URL: http://arxiv.org/abs/2502.20035v1
• Date: Thu, 27 Feb 2025 12:21:02 GMT
• Title: AsymLoRA: Harmonizing Data Conflicts and Commonalities in MLLMs
• Authors: Xuyang Wei, Chunlin Tian, Li Li,
• Abstract summary: AsymLoRA is a parameter-efficient tuning framework that unifies knowledge modularization and cross-modal coordination.<n>AsymLoRA consistently surpasses both vanilla LoRA, which captures only commonalities, and LoRA-MoE, which focuses solely on conflicts.
• Score: 5.018961516699825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effective instruction fine-tuning on diverse image-text datasets is crucial for developing a versatile Multimodal Large Language Model (MLLM), where dataset composition dictates the model's adaptability across multimodal tasks. However, complex datasets often contain inherent conflicts -- stemming from modality-specific optimization objectives -- and latent commonalities that enable cross-task transfer, which most existing approaches handle separately. To bridge this gap, we introduce AsymLoRA, a parameter-efficient tuning framework that unifies knowledge modularization and cross-modal coordination via asymmetric LoRA: task-specific low-rank projections (matrix B) that preserve distinct adaptation pathways for conflicting objectives, and a shared projection (matrix A) that consolidates cross-modal commonalities. Extensive evaluations demonstrate that AsymLoRA consistently surpasses both vanilla LoRA, which captures only commonalities, and LoRA-MoE, which focuses solely on conflicts, achieving superior model performance and system efficiency across diverse benchmarks.\href{Code}{https://github.com/Clin0212/HydraLoRA/blob/main/MLLM-HydraLoRA/README.md}.

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