Efficient Multimodal Infusion Tuning for Large Models

מושגי ליבה

The author introduces a new parameter-efficient multimodal tuning strategy, Multimodal Infusion Tuning (MiT), to integrate diverse modalities into large language models efficiently.

תקציר

Recent advancements in large-scale models have shown remarkable generalization capabilities in various tasks. Integrating multimodal processing into these models presents a significant challenge due to high computational burden. MiT leverages decoupled self-attention mechanisms within large language models to effectively integrate information from diverse modalities such as images and acoustics. The approach includes a novel adaptive rescaling strategy at the head level to optimize the representation of infused multimodal features. All foundation models are kept frozen during the tuning process to reduce computational burden, with only 2.5% of parameters being tunable. Experiments across various multimodal tasks showcase that MiT achieves state-of-the-art performance while significantly reducing computational overhead by 10% compared to previous methods.

סטטיסטיקה

"only 2.5% parameters are tunable"
"10% of previous methods"
"our proposed method requires only 0.47 TFLOPs"

ציטוטים

"We introduce a fine-grained tuning strategy for LLMs, named multimodal infusion tuning."
"Our approach achieves state-of-the-art performance on seven evaluated datasets."

תובנות מפתח מזוקקות מ:

Multimodal Infusion Tuning for Large Models

by Hao Sun,Yu S... ב- arxiv.org 03-11-2024

https://arxiv.org/pdf/2403.05060.pdf

שאלות מעמיקות

How does the linear infusion strategy in MiT contribute to reducing computational overhead

The linear infusion strategy in MiT contributes to reducing computational overhead by ensuring low memory consumption. This is achieved through the efficient integration of multimodal information into large language models (LLMs) with a linear complexity. By decoupling self-attention mechanisms and progressively infusing global representations from other modalities, such as images and acoustics, the infusion process is designed to be linear. This approach minimizes the computational burden associated with integrating diverse modalities into LLMs. Additionally, the use of an adaptive rescaling strategy at the head level further enhances efficiency by eliminating numerical instabilities and promoting collaboration between different modalities.

What potential limitations could arise from keeping all foundation models frozen during the tuning process

Keeping all foundation models frozen during the tuning process may lead to certain limitations. One potential limitation is that freezing all parameters in pretrained models restricts their adaptability to new data or tasks introduced during tuning. This rigidity could hinder the model's ability to generalize well across various scenarios or domains that were not explicitly covered in pretraining data. Another limitation could be related to fine-tuning specific aspects of pretrained models that might require adjustments based on task-specific requirements or evolving datasets. Without flexibility in modifying foundational parameters, there may be constraints on optimizing performance for complex tasks or novel challenges.

How can the concept of complex reasoning be further enhanced in future research related to multimodal understanding

To enhance complex reasoning in future research related to multimodal understanding, several approaches can be considered:

Integrating Hierarchical Reasoning: Implementing hierarchical reasoning structures within LLMs can enable them to understand relationships between concepts at different levels of abstraction.
Incorporating Contextual Cues: Enhancing contextual understanding by incorporating contextual cues from multiple modalities can improve complex reasoning capabilities.
Utilizing Reinforcement Learning: Introducing reinforcement learning techniques can help train models to make sequential decisions based on multimodal inputs for more nuanced reasoning processes.
Exploring Graph-based Models: Leveraging graph neural networks can capture intricate dependencies between entities and attributes for more sophisticated reasoning abilities.
Implementing Memory Mechanisms: Integrating memory-augmented architectures within LLMs can facilitate long-term context retention and support complex inference processes.

By exploring these avenues, researchers can advance the field of multimodal understanding towards more robust and nuanced complex reasoning capabilities across various applications and domains."