Summary of Less Is More: a Simple Yet Effective Token Reduction Method For Efficient Multi-modal Llms, by Dingjie Song et al.
Less is More: A Simple yet Effective Token Reduction Method for Efficient Multi-modal LLMs
by Dingjie Song, Wenjun Wang, Shunian Chen, Xidong Wang, Michael Guan, Benyou Wang
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The rapid advancements in Multimodal Large Language Models (MLLMs) have led to impressive performances across various domains. However, this progress is accompanied by significant resource consumption. To address this issue, we introduce Token Reduction using CLIP Metric (TRIM), a new approach that improves MLLM efficiency without sacrificing performance. Inspired by human attention patterns in Visual Question Answering (VQA) tasks, TRIM presents a fresh perspective on image token selection and reduction. We tested TRIM across 12 datasets, achieving significant reductions in computational overhead while maintaining consistent performance. This research marks a critical stride in efficient MLLM development, promoting greater accessibility and sustainability of high-performing models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models more efficient without losing their ability to perform well. Right now, these models use up a lot of resources because they’re so big and powerful. The authors came up with a new way called TRIM that helps reduce the amount of resources needed while keeping the performance the same. They tested it on 12 different datasets and showed that it works really well. This is important because it will make it easier to use these models in more places, making them more accessible and sustainable. |
Keywords
» Artificial intelligence » Attention » Question answering » Token
