# A Comprehensive Survey and Guide to Multimodal Large Language Models in Vision-Language Tasks

arXiv: https://arxiv.org/abs/2411.06284 · HTML: https://mlsanity.com/paper/2411.06284/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 15 of its 241 references that are in the map.
- First posted on arXiv 9 Nov 2024.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/)

## Builds on (most cited first, 15 of 15)

- [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023)
- [Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks](https://mlsanity.com/paper/2106.04489/) (2021)
- [Scene-LLM: Extending Language Model for 3D Visual Understanding and Reasoning](https://mlsanity.com/paper/2403.11401/) (2024)
- [Hallucination Augmented Contrastive Learning for Multimodal Large Language Model](https://mlsanity.com/paper/2312.06968/) (2023)
- [MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning](https://mlsanity.com/paper/2311.02303/) (2023)
- [RAMM: Retrieval-augmented Biomedical Visual Question Answering with Multi-modal Pre-training](https://mlsanity.com/paper/2303.00534/) (2023)
- [Masked Vision and Language Pre-training with Unimodal and Multimodal Contrastive Losses for Medical Visual Question Answering](https://mlsanity.com/paper/2307.05314/) (2023)
- [OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning](https://mlsanity.com/paper/2405.01533/) (2024)
- [Improving Zero-shot Visual Question Answering via Large Language Models with Reasoning Question Prompts](https://mlsanity.com/paper/2311.09050/) (2023)
- [Img-Diff: Contrastive Data Synthesis for Multimodal Large Language Models](https://mlsanity.com/paper/2408.04594/) (2024)
- [M2IST: Multi-Modal Interactive Side-Tuning for Efficient Referring Expression Comprehension](https://mlsanity.com/paper/2407.01131/) (2024)
- [MultiWay-Adapater: Adapting large-scale multi-modal models for scalable image-text retrieval](https://mlsanity.com/paper/2309.01516/) (2023)
- [SPT: Semi-Parametric Prompt Tuning for Multitask Prompted Learning](https://mlsanity.com/paper/2212.10929/) (2022)
- [Transforming LLMs into Cross-modal and Cross-lingual Retrieval Systems](https://mlsanity.com/paper/2404.01616/) (2024)
- [Let Storytelling Tell Vivid Stories: An Expressive and Fluent Multimodal Storyteller](https://mlsanity.com/paper/2403.07301/) (2024)

## Built on by (most cited first, 27 of 27)

- [A Survey of Token Compression for Efficient Multimodal Large Language Models](https://mlsanity.com/paper/2507.20198/) (2025)
- [Where MLLMs Attend and What They Rely On: Explaining Autoregressive Token Generation](https://mlsanity.com/paper/2509.22496/) (2025)
- [Building Trustworthy Multimodal AI: A Review of Fairness, Transparency, and Ethics in Vision-Language Tasks](https://mlsanity.com/paper/2504.13199/) (2025)
- [Toward Native Multimodal Modeling: A Roadmap](https://mlsanity.com/paper/2605.25343/) (2026)
- [Multi-modal Summarization in Model-Based Engineering: Automotive Software Development Case Study](https://mlsanity.com/paper/2503.04506/) (2025)
- [AVA-Bench: Atomic Visual Ability Benchmark for Vision Foundation Models](https://mlsanity.com/paper/2506.09082/) (2025)
- [Towards Understanding Modality Interaction in Multimodal Language Models via Partial Information Decomposition](https://mlsanity.com/paper/2606.00959/) (2026)
- [VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos](https://mlsanity.com/paper/2505.23693/) (2025)
- [CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical Contrastive Decoding](https://mlsanity.com/paper/2509.23379/) (2025)
- [Affective Multimodal Agents with Proactive Knowledge Grounding for Emotionally Aligned Marketing Dialogue](https://mlsanity.com/paper/2511.21728/) (2025)
- [HUMORCHAIN: Theory-Guided Multi-Stage Reasoning for Interpretable Multimodal Humor Generation](https://mlsanity.com/paper/2511.21732/) (2025)
- [ClinKD: Cross-Modal Clinical Knowledge Distiller For Multi-Task Medical Images](https://mlsanity.com/paper/2502.05928/) (2025)
- [Exploring Implicit Visual Misunderstandings in Multimodal Large Language Models through Attention Analysis](https://mlsanity.com/paper/2505.10541/) (2025)
- [Falcon: A Cross-Modal Evaluation Dataset for Comprehensive Safety Perception](https://mlsanity.com/paper/2509.23783/) (2025)
- [LatentRouter: Can We Choose the Right Multimodal Model Before Seeing Its Answer?](https://mlsanity.com/paper/2605.11301/) (2026)
- [LumiGen: An LVLM-Enhanced Iterative Framework for Fine-Grained Text-to-Image Generation](https://mlsanity.com/paper/2508.04732/) (2025)
- [Vision-Driven Prompt Optimization for Large Language Models in Multimodal Generative Tasks](https://mlsanity.com/paper/2501.02527/) (2025)
- [VisuCraft: Enhancing Large Vision-Language Models for Complex Visual-Guided Creative Content Generation via Structured Information Extraction](https://mlsanity.com/paper/2508.02890/) (2025)
- [Structuring Reasoning for Complex Rules Beyond Flat Representations](https://mlsanity.com/paper/2510.05134/) (2025)
- [When Harmful Content Gets Camouflaged: Unveiling Perception Failure of LVLMs with CamHarmTI](https://mlsanity.com/paper/2512.03087/) (2025)
- [LLaVA-CKD: Bottom-Up Cascaded Knowledge Distillation for Vision-Language Models](https://mlsanity.com/paper/2605.10641/) (2026)
- [From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Models](https://mlsanity.com/paper/2606.26196/) (2026)
- [EVLA: An Electro-Aware Multimodal Assistant for Physically-Grounded Driving Reasoning and Control](https://mlsanity.com/paper/2606.28938/) (2026)
- [DAIN: Dynamic Agent-Based Interaction Network for Efficient and Collaborative Multimodal Reasoning](https://mlsanity.com/paper/2606.30189/) (2026)
- [Beyond Skepticism: Evaluating LLMs Pedagogical Intent Reasoning with the Adaptive Pedagogical Vigilance Framework](https://mlsanity.com/paper/2607.01581/) (2026)
- [From Modalities to Propositions: A Language-Centric Framework for Multimodal Intelligence](https://mlsanity.com/paper/2607.16560/) (2026)
- [Rethinking CD: A Reproducibility Study and Extension on the Ineffectiveness of Contrastive Decoding at Mitigating Object Hallucinations in MLLMs](https://mlsanity.com/paper/2607.25196/) (2026)

## Abstract (arXiv)

This survey and application guide to multimodal large language models(MLLMs) explores the rapidly developing field of MLLMs, examining their architectures, applications, and impact on AI and Generative Models. Starting with foundational concepts, we delve into how MLLMs integrate various data types, including text, images, video and audio, to enable complex AI systems for cross-modal understanding and generation. It covers essential topics such as training methods, architectural components, and practical applications in various fields, from visual storytelling to enhanced accessibility. Through detailed case studies and technical analysis, the text examines prominent MLLM implementations while addressing key challenges in scalability, robustness, and cross-modal learning. Concluding with a discussion of ethical considerations, responsible AI development, and future directions, this authoritative resource provides both theoretical frameworks and practical insights. It offers a balanced perspective on the opportunities and challenges in the development and deployment of MLLMs, and is highly valuable for researchers, practitioners, and students interested in the intersection of natural language processing and computer vision.