Adapting Multi-modal Large Language Model to Concept Drift From Pre-training Onwards

TL;DR

This paper introduces a unified framework and a T-distribution based drift adapter to improve multi-modal large language models' ability to adapt to concept drift in streaming data, enhancing their robustness and accuracy.

Contribution

It extends concept drift theory to multi-modal vision-language models and proposes a novel drift adapter to mitigate biases from distribution changes.

Findings

Enhanced image-text alignment accuracy in pre-training.

Improved downstream task performance in open-world scenarios.

Validated effectiveness on newly created OpenMMlo datasets.

Abstract

Multi-modal Large Language Models (MLLMs) frequently face challenges from concept drift when dealing with real-world streaming data, wherein distributions change unpredictably. This mainly includes gradual drift due to long-tailed data and sudden drift from Out-Of-Distribution (OOD) data, both of which have increasingly drawn the attention of the research community. While these issues have been extensively studied in the individual domain of vision or language, their impacts on MLLMs in concept drift settings remain largely underexplored. In this paper, we reveal the susceptibility and vulnerability of Vision-Language (VL) models to significant biases arising from gradual drift and sudden drift, particularly in the pre-training. To effectively address these challenges, we propose a unified framework that extends concept drift theory to the multi-modal domain, enhancing the adaptability of the VL model to unpredictable distribution changes. Additionally, a T-distribution based drift adapter is proposed to effectively mitigate the bias induced by the gradual drift, which also facilitates the model in distinguishing sudden distribution changes through explicit distribution modeling. Extensive experiments demonstrate our method enhances the efficiency and accuracy of image-text alignment in the pre-training of VL models, particularly in the concept drift scenario. Moreover, various downstream tasks exhibit significant improvements in our model's ability to adapt to the long-tailed open world. Furthermore, we create a set of multi-modal datasets called OpenMMlo, specifically tailored for the long-tailed open-world setting, to validate our findings. To foster the development of the multi-modal community, we have made both OpenMMlo datasets and our code publicly available at: https://github.com/XiaoyuYoung/ConceptDriftMLLMs.