Report on Current Developments in Multimodal Large Language Models (MLLMs)
General Direction of the Field
The field of Multimodal Large Language Models (MLLMs) is rapidly evolving, with a strong emphasis on enhancing the models' ability to understand and generate content across multiple modalities, including text, images, speech, and video. Recent advancements are pushing the boundaries of what these models can achieve, moving towards more comprehensive and versatile capabilities that allow for any-to-any understanding and generation. This shift is driven by the need for models that can handle complex, real-world tasks that require the integration of multiple data types.
One of the key trends is the development of foundation models that are trained on a mixture of discrete tokens across various modalities. These models are designed to be end-to-end and autoregressive, enabling them to generate coherent and contextually relevant outputs across different data types. The training processes for these models are becoming increasingly sophisticated, involving multiple stages such as alignment pre-training, interleaved pre-training, and comprehensive supervised fine-tuning. This multi-stage approach allows for the integration of diverse data sources and the optimization of model performance across a wide range of tasks.
Another significant trend is the focus on data-centric approaches to model training. Researchers are increasingly recognizing the importance of high-quality, diverse datasets in the training of MLLMs. This includes the use of synthetic data, specialized datasets for specific tasks (e.g., video understanding, mobile UI understanding), and the optimization of data mixtures to enhance model capabilities. The goal is to create models that are not only powerful but also adaptable and capable of handling a variety of real-world scenarios.
The field is also seeing a growing interest in addressing specific challenges, such as the understanding of occluded objects in images. This involves the development of novel visual encoders and the creation of large-scale datasets that include occluded objects, enabling models to better understand and describe these elements in visual-language multimodal tasks.
Noteworthy Papers
MIO: A Foundation Model on Multimodal Tokens: Introduces a novel foundation model capable of end-to-end, autoregressive generation across speech, text, images, and videos, showcasing advanced capabilities in interleaved multimodal generation and reasoning.
MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning: Presents a new family of MLLMs designed to enhance text-rich image understanding and multi-image reasoning, emphasizing the importance of data curation and training strategies.
OCC-MLLM: Empowering Multimodal Large Language Model For the Understanding of Occluded Objects: Addresses the challenge of understanding occluded objects in images through a novel visual encoder and a large-scale dataset, advancing the field's ability to handle complex visual-language tasks.
