Efficient Multi-modal Long Context Learning for Training-free Adaptation (2505.19812v1)

Abstract: Traditional approaches to adapting multi-modal LLMs (MLLMs) to new tasks have relied heavily on fine-tuning. This paper introduces Efficient Multi-Modal Long Context Learning (EMLoC), a novel training-free alternative that embeds demonstration examples directly into the model input. EMLoC offers a more efficient, flexible, and scalable solution for task adaptation. Because extremely lengthy inputs introduce prohibitive computational and memory overhead, EMLoC contributes a chunk-wise compression mechanism combined with layer-wise adaptive pruning. It condenses long-context multimodal inputs into compact, task-specific memory representations. By adaptively pruning tokens at each layer under a Jensen-Shannon divergence constraint, our method achieves a dramatic reduction in inference complexity without sacrificing performance. This approach is the first to seamlessly integrate compression and pruning techniques for multi-modal long-context learning, offering a scalable and efficient solution for real-world applications. Extensive experiments on diverse vision-language benchmarks demonstrate that EMLoC achieves performance on par with or superior to naive long-context approaches. Our results highlight the potential of EMLoC as a groundbreaking framework for efficient and flexible adaptation of multi-modal models in resource-constrained environments. Codes are publicly available at https://github.com/Zehong-Ma/EMLoC.

Efficient Multi-modal Long Context Learning for Training-free Adaptation

The paper introduces Efficient Multi-modal Long Context Learning (EMLoC), a method for adapting multi-modal LLMs (MLLMs) to new tasks without the need for training. Unlike traditional approaches that require fine-tuning, EMLoC embeds demonstration examples directly into the model input, combining chunk-wise compression with layer-wise adaptive pruning to achieve efficient inference. This strategy reduces computational costs by condensing lengthy contexts into compact task-specific memory representations, thereby mitigating memory overhead while maintaining high performance.

EMLoC employs a layered adaptive pruning method, a novel approach where tokens are pruned based on their contribution to the information content of the memory cached by the model, assessed through Jensen-Shannon divergence. The process dynamically adapts the number of tokens retained at each layer according to their importance, optimized using a greedy search algorithm. This enables significant reduction in context length, demonstrated experimentally across several vision-language benchmarks such as ImageNet100, ScreenSpot, and YouCook2. Notably, EMLoC's efficacy extends across diverse tasks, achieving performance equivalent to or surpassing naive long-context solutions without incurring the computational burden typically associated with such approaches.

The experiments revealed that EMLoC decreases inference costs substantially. For example, on ImageNet100, it compresses the context length by 77% while maintaining performance akin to the baseline. This highlights its practical value in resource-constrained environments, allowing AI applications to leverage long-context learning efficiently.

The implications of this research are noteworthy for the future of AI model adaptability and efficiency. The training-free nature of EMLoC implies a reduction in the necessity for computational resources traditionally occupied by fine-tuning processes. It opens pathways for deploying AI models in environments with limited computational capacity, facilitating broader accessibility and potentially democratizing AI technologies.

Theoretically, the paper establishes a well-founded method for calculating information loss due to context compression, providing assurance that the adaptation method remains practical within defined divergence constraints. This theoretical framework can guide further development of training-free methodologies, fostering innovation in efficient model adaptation strategies.

Future research may explore expanded applications of EMLoC across different AI domains, such as natural language processing or audio-visual integration, to test its robustness and efficacy beyond multi-modal tasks. Integration with different compression and pruning techniques could further optimize performance, aligning with advancements in hardware capabilities and algorithmic efficiencies.

In conclusion, EMLoC presents an optimized approach for efficient, training-free adaptation of multi-modal models, contributing significant advancements in context utilization and resource optimization. As the field progresses, EMLoC represents a promising direction towards scalable AI applications, catering to the growing demand for adaptable and computationally efficient AI systems.