AlignGPT: Multi-modal Large Language Models with Adaptive Alignment
  Capability
Authors: name: Fei Zhao, name: Taotian Pang, name: Chunhui Li, name: Zhen Wu, name: Junjie Guo, name: Shangyu Xing, name: Xinyu Dai

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.