 [2405.14129] AlignGPT: Multi-modal Large Language Models with Adaptive Alignment Capability




























  








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Computer Science > Computation and Language


arXiv:2405.14129 (cs)
    




  [Submitted on 23 May 2024]
Title:AlignGPT: Multi-modal Large Language Models with Adaptive Alignment Capability
Authors:Fei Zhao, Taotian Pang, Chunhui Li, Zhen Wu, Junjie Guo, Shangyu Xing, Xinyu Dai View a PDF of the paper titled AlignGPT: Multi-modal Large Language Models with Adaptive Alignment Capability, by Fei Zhao and 6 other authors
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Abstract: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.
    


 
Comments:
Code and models are available at $\href{this https URL}{\textit{this https URL}}$


Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)

Cite as:
arXiv:2405.14129 [cs.CL]


 
(or 
arXiv:2405.14129v1 [cs.CL] for this version)
          
 
 

https://doi.org/10.48550/arXiv.2405.14129



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                arXiv-issued DOI via DataCite
              







Submission history From: Fei Zhao [view email]       [v1]
        Thu, 23 May 2024 03:07:56 UTC (530 KB)



 

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