An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models
<|reference_start|>An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models: Multimodal large language models (MLLMs) fine-tuned with multimodal instruction datasets have demonstrated remarkable capabilities in multimodal tasks. However, fine-tuning all parameters of MLLMs has become challenging as they usually contain billions of parameters. To address this issue, we study parameter-efficient fine-tuning (PEFT) methods for MLLMs. We aim to identify effective methods for enhancing the performance of MLLMs in scenarios where only a limited number of parameters are trained. This paper conducts empirical studies using four popular PEFT methods to fine-tune the LLM component of open-source MLLMs. We present a comprehensive analysis that encompasses various aspects, including the impact of PEFT methods on various models, parameters and location of the PEFT module, size of fine-tuning data, model stability based on PEFT methods, MLLM's generalization, and hallucination. We evaluated four PEFT methods on seven datasets from two different categories: unseen and seen datasets. Across all experiments, we show that the adapter is the best-performing PEFT method. At the same time, fine-tuning the connector layers leads to improved performance in most MLLMs. Code and data are available at https://github.com/alenai97/PEFT-MLLM.git.<|reference_end|>
@article{zhou2024an,
    title={An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal
  Large Language Models},
    author={Xiongtao Zhou, Jie He, Yuhua Ke, Guangyao Zhu, V'ictor
  Guti'errez-Basulto, Jeff Z. Pan},
    journal={arXiv preprint arXiv:2406.05130},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05130},
    primaryClass={cs.CL}
}