Sparsity Meets Similarity: Leveraging Long-Tail Distribution for Dynamic Optimized Token Representation in Multimodal Large Language Models

• URL: http://arxiv.org/abs/2409.01162v2
• Date: Wed, 12 Feb 2025 04:35:28 GMT
• Title: Sparsity Meets Similarity: Leveraging Long-Tail Distribution for Dynamic Optimized Token Representation in Multimodal Large Language Models
• Authors: Gaotong Yu, Yi Chen, Jian Xu,
• Abstract summary: multimodal large language models (MM-LLMs) have achieved significant success in various tasks. Main computational burden arises from processingd text and visual tokens. We propose a dynamic pruning algorithm that identifies the inflection point in the visual CLS token similarity curve.
• Score: 6.467840081978855
• License:
• Abstract: Recently, multimodal large language models (MM-LLMs) have achieved significant success in various tasks, but their high computational costs limit widespread application. The main computational burden arises from processing concatenated text and visual tokens in the LLM layer, where input token length directly affects efficiency. Our analysis of visual tokens reveals that their similarity to the CLS token follows a long-tail distribution, with only a few showing high similarity. To address this, we propose a dynamic pruning algorithm that identifies the inflection point in the visual CLS token similarity curve, enabling effective trimming of visual markers to accelerate model performance. Additionally, we perform a second round of pruning in the LLM layer, filtering out low-correlation tokens through the interaction between visual and textual features. Experimental results demonstrate that our method achieves performance comparable to the original while utilizing only 22% of the original token quantity. Our source code will be made publicly available upon acceptance.

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