Key Points
1. The paper discusses the importance of incorporating interleaved pre-training data for MLLMs to achieve desired outcomes. It highlights the advantages of using a combination of different types of data for the pre-training of models.
2. The role of Instruction Tuning (IT) in refining efficient MLLMs' capability to accurately interpret user instructions and carry out tasks is emphasized. It mentions the connection of IT to the concept of multi-task prompting.
3. The paper outlines frequently used pre-training datasets and emphasizes the importance of high-quality IT data derived from task-specific datasets for effective training of MLLMs.
4. It discusses the challenges of utilizing multi-task datasets for complex real-world situations and presents research that explores the use of self-instruction for efficient data generation from a limited number of hand-annotated samples.
5. The paper presents a comparison of 22 MLLMs across 14 well-established Visual Language benchmarks, demonstrating the effectiveness of different models in various scenarios.
6. It highlights the significance of efficient MLLMs in biomedicine applications, such as medical question answering, medical image classification, and multimodal generative AI for the biomedical domain.
7. The paper discusses the challenges faced by current chart understanding models and presents efficient solutions for fine-grained visual perception and visual information compression for document-oriented MLLMs.
8. It emphasizes the importance of intelligent video understanding for various real-world applications and presents different approaches designed for video comprehension tasks.
9. The paper concludes by summarizing the current challenges and prospects for efficient MLLMs, including the need for models capable of handling increased numbers of multimodal tokens, supporting a wider range of input modalities, and the potential applications of efficient MLLMs in areas such as robotics, automation, and artificial intelligence.
Summary
The paper provides a comprehensive survey of the current state of efficient Multimodal Large Language Models (MLLMs), focusing on efficient structures and strategies. The authors start by discussing representative efficient MLLMs and provide a timeline of their development. They then delve into the research state of efficient MLLM structures and strategies, covering different applications of these models. An interesting aspect is the discussion of the limitations of current efficient MLLM research and the proposal of promising future directions. Furthermore, the paper refers to a GitHub repository for further details.
The survey highlights the approaches taken by various MLLMs, such as SPHINX-X, ALLaVA, VILA's research, and others, in generating text-based or multimodal instruction-following data with a limited number of hand-annotated samples. These MLLMs leverage diverse and fine-grained datasets to prompt GPT-4V with marked images and tailored domain-specific guidelines to generate captions with image overviews, regional details, and object relationship insights. The paper also discusses their performance on a range of scenarios vis-a-vis other multimodal tasks.
In addition, the authors present a detailed comparison of the effectiveness of 22 MLLMs across 14 well-established Visual Language (VL) benchmarks, and they incorporate a comparison of results from 13 prominent and larger MLLMs. The analysis reveals the comprehensive evaluation of efficient MLLMs across VL benchmarks and the relative performance compared to larger MLLMs.
The paper further expands the application of efficient MLLMs to incorporate several downstream tasks, including medical analysis, document understanding, and video comprehension. Notably, the mixture of Expert Tuning has effectively enhanced the performance of general MLLMs with fewer parameters, and models such as MoE-TinyMed and LLaVA-Rad have demonstrated rapid performance, showcasing superior efficiency and effectiveness in private settings.
Furthermore, the paper discusses efficient MLLMs' challenges in processing extended-context multimodal information and their limitations to accepting single images. It also emphasizes expanding the models' scope to accommodate a wider array of input modalities and augmenting their generative capacities to bolster their multifunctionality and widen their applicability.
Overall, the survey paper provides a detailed overview of efficient Multimodal Large Language Models, covering their timeline, research state, applications, limitations, and future directions. It encompasses a wide range of efficient structures and strategies and their applications, contributing to a comprehensive understanding of the advancements in the field of efficient MLLMs.
Reference: https://arxiv.org/abs/2405.10739v1