The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective

TL;DR

This survey explores the interconnected development of multi-modal large language models and their data, emphasizing how data quality and model capabilities mutually enhance each other across different development stages.

Contribution

It provides a systematic review of data-centric approaches in MLLMs, highlighting the co-development process and offering a clear framework for future research.

Findings

Data quality significantly impacts MLLM performance.

MLLMs can facilitate the development of multi-modal data.

A framework for data-model co-development stages is proposed.

Abstract

The rapid development of large language models (LLMs) has been witnessed in recent years. Based on the powerful LLMs, multi-modal LLMs (MLLMs) extend the modality from text to a broader spectrum of domains, attracting widespread attention due to the broader range of application scenarios. As LLMs and MLLMs rely on vast amounts of model parameters and data to achieve emergent capabilities, the importance of data is receiving increasingly widespread attention and recognition. Tracing and analyzing recent data-oriented works for MLLMs, we find that the development of models and data is not two separate paths but rather interconnected. On the one hand, vaster and higher-quality data contribute to better performance of MLLMs; on the other hand, MLLMs can facilitate the development of data. The co-development of multi-modal data and MLLMs requires a clear view of 1) at which development stages of MLLMs specific data-centric approaches can be employed to enhance certain MLLM capabilities, and 2) how MLLMs, utilizing those capabilities, can contribute to multi-modal data in specific roles. To promote the data-model co-development for MLLM community, we systematically review existing works related to MLLMs from the data-model co-development perspective. A regularly maintained project associated with this survey is accessible at https://github.com/modelscope/data-juicer/blob/main/docs/awesome_llm_data.md.