A comprehensive survey and guide to multimodal large language models in vision–language tasks

Analysis

Why This Paper Matters

Multimodal large language models (MLLMs) are rapidly transforming how AI systems process and generate information across text and vision. This survey provides a much-needed structured overview of the field, which has grown explosively in recent years. By organizing the landscape into clear categories—architectures, training strategies, and applications—the paper helps researchers and practitioners navigate the complex and often fragmented literature.

The timing is crucial: as MLLMs move from research prototypes to deployed products, a comprehensive guide that synthesizes best practices and identifies open problems is invaluable. The survey not only catalogs existing methods but also offers practical guidance on model selection and deployment, making it a useful resource for both academic and industrial audiences.

Technical Contributions

The paper's main technical contributions include:

• Taxonomy of MLLM architectures: It categorizes models based on how they fuse visual and textual modalities, such as cross-attention, co-attention, and unified transformer designs.
• Training paradigm analysis: It reviews two-stage training (pretraining on large-scale data followed by instruction tuning) and end-to-end approaches, highlighting trade-offs in data efficiency and performance.
• Task-specific review: It provides detailed coverage of image captioning and visual question answering, including recent advances in zero-shot and few-shot settings.
• Evaluation and benchmarks: It compiles a list of standard datasets and metrics, such as CIDEr, BLEU, and accuracy on VQA benchmarks, and discusses their limitations.
• Open challenges: It identifies issues like hallucination, reasoning gaps, and robustness, which are critical for future research.

Results

As a survey, the paper does not present new experimental results. Instead, it synthesizes findings from numerous studies, noting that MLLMs have achieved state-of-the-art performance on many vision-language benchmarks, often surpassing task-specific models. For example, models like GPT-4V and LLaVA have demonstrated strong zero-shot capabilities in image captioning and VQA. However, the survey also notes that performance varies significantly across tasks and that many models still struggle with fine-grained reasoning and spatial understanding.

The paper emphasizes that while MLLMs excel at generating fluent and contextually relevant responses, they often lack interpretability and can produce hallucinated content. These qualitative observations are drawn from the surveyed literature rather than new experiments.

Significance

The broader impact of this survey lies in its role as a reference point for the AI community. By consolidating knowledge, it accelerates the learning curve for new researchers and helps practitioners make informed decisions about model adoption. It also highlights critical gaps—such as the need for more robust evaluation metrics and better handling of compositional reasoning—that will shape future research agendas.

As MLLMs become integral to applications like assistive technologies, autonomous systems, and content creation, having a clear map of the field is essential. This survey contributes to that goal, fostering a more structured and collaborative advancement of multimodal AI.