Leveraging Visual Tokens for Extended Text Contexts in Multi-Modal Learning

Alex Jinpeng Wang; Linjie Li; Yiqi Lin; Min Li; Lijuan Wang; Mike Zheng Shou

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arXiv · 2406.02547

Leveraging Visual Tokens for Extended Text Contexts in Multi-Modal Learning

Alex Jinpeng Wang·Linjie Li·Yiqi Lin·Min Li·Lijuan Wang·Mike Zheng Shou

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Abstract

Training models with longer in-context lengths is a significant challenge for multimodal model due to substantial GPU memory and computational costs. This exploratory study does not present state-of-the-art models; rather, it introduces an innovative method designed to increase in-context text length in multi-modality large language models (MLLMs) efficiently. We present Visualized In-Context Text Processing (VisInContext), which processes long in-context text using visual tokens. This technique significantly reduces GPU memory usage and floating point operations (FLOPs) for both training and inferenceing stage. For instance, our method expands the pre-training in-context text length from 256 to 2048 tokens with nearly same FLOPs for a 56 billion parameter MOE model. Experimental results demonstrate that model trained with VisInContext delivers superior performance on common downstream benchmarks for in-context few-shot evaluation. Additionally, VisInContext is complementary to existing methods for increasing in-context text length and enhances document understanding capabilities, showing great potential in document QA tasks and sequential document retrieval.

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BibTeX RIS

Alex Jinpeng Wang, Linjie Li, Yiqi Lin, Min Li, Lijuan Wang, Mike Zheng Shou. 2024-06-04. Leveraging Visual Tokens for Extended Text Contexts in Multi-Modal Learning. https://arxiv.org/abs/2406.02547

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