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<!DOCTYPE html>
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content="AlignGPT: Multi-modal Large Language Models with Adaptive Alignment Capability">
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<title>AlignGPT: Multi-modal Large Language Models with Adaptive Alignment Capability</title>
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<h1 class="title is-1 publication-title">AlignGPT: Multi-modal Large Language Models with Adaptive Alignment Capability</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://scholar.google.com/citations?user=V01xzWQAAAAJ&hl=zh-CN">Fei Zhao</a><sup>*</sup>,</span>
<span class="author-block">
<a href="">Taotian Pang</a><sup>*</sup>,
</span>
<span class="author-block">
<a href="">Chunhui Li</a>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=IoGlgtoAAAAJ&hl=zh-CN">Zhen Wu</a>,
</span>
<span class="author-block">
<a href="">Junjie Guo</a>,
</span>
<span class="author-block">
<a href="">Shangyu Xing</a>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=zpWB1CgAAAAJ&hl=zh-CN">Xinyu Dai</a>
</span>
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<div class="is-size-5 publication-authors">
<span class="author-block">Nanjing University</span>
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<span class="author-block"><sup>*</sup>Equal Contribution,</span>
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<h2 class="title is-3">Abstract</h2>
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<p>
Multimodal Large Language Models (MLLMs) are widely regarded as crucial in the exploration of Artificial General Intelligence (AGI). The core of MLLMs lies in their capability to achieve cross-modal alignment. To attain this goal, current MLLMs typically follow a two-phase training paradigm: the pre-training phase and the instruction-tuning phase. Despite their success, there are shortcomings in the modeling of alignment capabilities within these models. Firstly, during the pre-training phase, the model usually assumes that all image-text pairs are uniformly aligned, but in fact the degree of alignment between different image-text pairs is inconsistent. Secondly, the instructions currently used for finetuning incorporate a variety of tasks, different tasks's instructions usually require different levels of alignment capabilities, but previous MLLMs overlook these differentiated alignment needs. To tackle these issues, we propose a new multimodal large language model AlignGPT. In the pre-training stage, instead of treating all image-text pairs equally, we assign different levels of alignment capabilities to different image-text pairs. Then, in the instruction-tuning phase, we adaptively combine these different levels of alignment capabilities to meet the dynamic alignment needs of different instructions. Extensive experimental results show that our model achieves competitive performance on 12 benchmarks.
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<h2 class="title is-3">Performance</h2>
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<img src="static/images/result1.png" alt="Performance of image caption and visual question answering" style="width: 100%; height: auto">
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<h2 class="title is-3"> Qualitative Results</h2>
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<h2 class="title">BibTeX</h2>
<pre><code>@misc{zhao2024aligngpt,
title={AlignGPT: Multi-modal Large Language Models with Adaptive Alignment Capability},
author={Fei Zhao and Taotian Pang and Chunhui Li and Zhen Wu and Junjie Guo and Shangyu Xing and Xinyu Dai},
year={2024},
eprint={2405.14129},
archivePrefix={arXiv},
primaryClass={cs.CL}
}</code></pre>
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