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Less is More: A Simple yet Effective Token Reduction Method for Efficient Multi-modal LLMs

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arxiv 2409.10994 v3 pith:NAGBSUOL submitted 2024-09-17 cs.CL cs.AIcs.CVcs.MM

Dingjie Song , Wenjun Wang , Shunian Chen , Xidong Wang , Michael Guan , Benyou Wang This is my paper · ORCID
classification cs.CLcs.AIcs.CVcs.MM
keywords reductionmodelstrimacrossefficientmethodmllmsperformance
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The rapid advancement of Multimodal Large Language Models (MLLMs) has led to remarkable performances across various domains. However, this progress is accompanied by a substantial surge in the resource consumption of these models. We address this pressing issue by introducing a new approach, Token Reduction using CLIP Metric (TRIM), aimed at improving the efficiency of MLLMs without sacrificing their performance. Inspired by human attention patterns in Visual Question Answering (VQA) tasks, TRIM presents a fresh perspective on the selection and reduction of image tokens. The TRIM method has been extensively tested across 12 datasets, and the results demonstrate a significant reduction in computational overhead while maintaining a consistent level of performance. This research marks a critical stride in efficient MLLM development, promoting greater accessibility and sustainability of high-performing models.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Grounding-Aware Token Pruning: Recovering from Drastic Performance Drops in Visual Grounding Caused by Pruning

    cs.CV 2025-06 conditional novelty 6.0 of 10

    Pruning visual tokens degrades visual grounding because position IDs become misaligned; preserving the original position IDs recovers most of the lost accuracy with no extra cost.

  2. Beyond Attention or Similarity: Maximizing Conditional Diversity for Token Pruning in MLLMs

    cs.CV 2025-06 conditional novelty 5.0 of 10

    CDPruner prunes visual tokens in MLLMs by maximizing instruction-conditioned diversity via a determinantal point process, preserving accuracy at high reduction ratios.

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