EE-MLLM: A Data-Efficient and Compute-Efficient Multimodal Large Language Model

• URL: http://arxiv.org/abs/2408.11795v2
• Date: Mon, 9 Sep 2024 18:57:01 GMT
• Title: EE-MLLM: A Data-Efficient and Compute-Efficient Multimodal Large Language Model
• Authors: Feipeng Ma, Yizhou Zhou, Hebei Li, Zilong He, Siying Wu, Fengyun Rao, Yueyi Zhang, Xiaoyan Sun,
• Abstract summary: We introduce the Data-Efficient and Compute-Efficient Multimodal Large Language Model (EE-MLLM) EE-MLLM achieves both data and compute efficiency without introducing additional modules or learnable parameters. Experimental results demonstrate the effectiveness of EE-MLLM across a range of benchmarks.
• Score: 14.767055057048855
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the realm of multimodal research, numerous studies leverage substantial image-text pairs to conduct modal alignment learning, transforming Large Language Models (LLMs) into Multimodal LLMs and excelling in a variety of visual-language tasks. The prevailing methodologies primarily fall into two categories: self-attention-based and cross-attention-based methods. While self-attention-based methods offer superior data efficiency due to their simple MLP architecture, they often suffer from lower computational efficiency due to concatenating visual and textual tokens as input for LLM. Conversely, cross-attention-based methods, although less data-efficient due to additional learnable parameters, exhibit higher computational efficiency by avoiding long sequence input for LLM. To address these trade-offs, we introduce the Data-Efficient and Compute-Efficient Multimodal Large Language Model (EE-MLLM). Without introducing additional modules or learnable parameters, EE-MLLM achieves both data and compute efficiency. Specifically, we modify the original self-attention mechanism in MLLM to a composite attention mechanism. This mechanism has two key characteristics: 1) Eliminating the computational overhead of self-attention within visual tokens to achieve compute efficiency, and 2) Reusing the weights on each layer of LLM to facilitate effective modality alignment between vision and language for data efficiency. Experimental results demonstrate the effectiveness of EE-MLLM across a range of benchmarks, including general-purpose datasets like MMBench and SeedBench, as well as fine-grained tasks such as TextVQA and DocVQA.

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