# Survey of Large Multimodal Model Datasets, Application Categories and Taxonomy

arXiv: https://arxiv.org/abs/2412.17759 · HTML: https://mlsanity.com/paper/2412.17759/

## Key facts

- Cited by 17 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 42 of its 63 references that are in the map.
- First posted on arXiv 23 Dec 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, 30 of 42)

- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts](https://mlsanity.com/paper/2102.08981/) (2021)
- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://mlsanity.com/paper/1906.03327/) (2019)
- [Vision-Language Models for Vision Tasks: A Survey](https://mlsanity.com/paper/2304.00685/) (2023)
- [ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks](https://mlsanity.com/paper/1912.01734/) (2019)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation](https://mlsanity.com/paper/2307.06942/) (2023)
- [TVQA: Localized, Compositional Video Question Answering](https://mlsanity.com/paper/1809.01696/) (2018)
- [OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation](https://mlsanity.com/paper/2407.02371/) (2024)
- [SQA3D: Situated Question Answering in 3D Scenes](https://mlsanity.com/paper/2210.07474/) (2022)
- [MM-LLMs: Recent Advances in MultiModal Large Language Models](https://mlsanity.com/paper/2401.13601/) (2024)
- [MIMIC-IT: Multi-Modal In-Context Instruction Tuning](https://mlsanity.com/paper/2306.05425/) (2023)
- [SpaceNet: A Remote Sensing Dataset and Challenge Series](https://mlsanity.com/paper/1807.01232/) (2018)
- [LAMM: Language-Assisted Multi-Modal Instruction-Tuning Dataset, Framework, and Benchmark](https://mlsanity.com/paper/2306.06687/) (2023)
- [SVIT: Scaling up Visual Instruction Tuning](https://mlsanity.com/paper/2307.04087/) (2023)
- [OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web](https://mlsanity.com/paper/2402.17553/) (2024)
- [CATER: A diagnostic dataset for Compositional Actions and TEmporal Reasoning](https://mlsanity.com/paper/1910.04744/) (2019)
- [AISHELL-2: Transforming Mandarin ASR Research Into Industrial Scale](https://mlsanity.com/paper/1808.10583/) (2018)
- [Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions](https://mlsanity.com/paper/2209.03430/) (2022)
- [Retrieving Multimodal Information for Augmented Generation: A Survey](https://mlsanity.com/paper/2303.10868/) (2023)
- [A Survey of Multimodal Large Language Model from A Data-centric Perspective](https://mlsanity.com/paper/2405.16640/) (2024)
- [Vript: A Video Is Worth Thousands of Words](https://mlsanity.com/paper/2406.06040/) (2024)
- [M3ED: Multi-modal Multi-scene Multi-label Emotional Dialogue Database](https://mlsanity.com/paper/2205.10237/) (2022)

## Built on by (most cited first, 17 of 17)

- [Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning](https://mlsanity.com/paper/2502.02871/) (2025)
- [Tokenization Matters: Improving Zero-Shot NER for Indic Languages](https://mlsanity.com/paper/2504.16977/) (2025)
- [MVTamperBench: Evaluating Robustness of Vision-Language Models](https://mlsanity.com/paper/2412.19794/) (2024)
- [Aligning LLMs for Multilingual Consistency in Enterprise Applications](https://mlsanity.com/paper/2509.23659/) (2025)
- [FS-DAG: Few Shot Domain Adapting Graph Networks for Visually Rich Document Understanding](https://mlsanity.com/paper/2505.17330/) (2025)
- [Clinical QA 2.0: Multi-Task Learning for Answer Extraction and Categorization](https://mlsanity.com/paper/2502.13108/) (2025)
- [Can LLMs Narrate Tabular Data? An Evaluation Framework for Natural Language Representations of Text-to-SQL System Outputs](https://mlsanity.com/paper/2510.23854/) (2025)
- [IndicJR: A Judge-Free Benchmark of Jailbreak Robustness in South Asian Languages](https://mlsanity.com/paper/2602.16832/) (2026)
- [Multimodal Representation Learning and Fusion](https://mlsanity.com/paper/2506.20494/) (2025)
- [RCI: A Score for Evaluating Global and Local Reasoning in Multimodal Benchmarks](https://mlsanity.com/paper/2509.23673/) (2025)
- [FlexDoc: Parameterized Sampling for Diverse Multilingual Synthetic Documents for Training Document Understanding Models](https://mlsanity.com/paper/2510.02133/) (2025)
- [LLM-Guided Lifecycle-Aware Clustering of Multi-Turn Customer Support Conversations](https://mlsanity.com/paper/2601.04388/) (2026)
- [Do Image-Text Metrics Respect Semantic Invariances?](https://mlsanity.com/paper/2605.24702/) (2026)
- [PCRI: Measuring Context Robustness in Multimodal Models for Enterprise Applications](https://mlsanity.com/paper/2509.23879/) (2025)
- [Scaling-Aware Data Selection for End-to-End Autonomous Driving Systems](https://mlsanity.com/paper/2604.08366/) (2026)
- [Dynamic Adversarial Reinforcement Learning for Robust Multimodal Large Language Models](https://mlsanity.com/paper/2602.22227/) (2026)
- [Test-Time Coverage: Test-Conditioned Data Curation for Deployment-Aware Learning](https://mlsanity.com/paper/2607.22697/) (2026)

## Abstract (arXiv)

Multimodal learning, a rapidly evolving field in artificial intelligence, seeks to construct more versatile and robust systems by integrating and analyzing diverse types of data, including text, images, audio, and video. Inspired by the human ability to assimilate information through many senses, this method enables applications such as text-to-video conversion, visual question answering, and image captioning. Recent developments in datasets that support multimodal language models (MLLMs) are highlighted in this overview. Large-scale multimodal datasets are essential because they allow for thorough testing and training of these models. With an emphasis on their contributions to the discipline, the study examines a variety of datasets, including those for training, domain-specific tasks, and real-world applications. It also emphasizes how crucial benchmark datasets are for assessing models' performance in a range of scenarios, scalability, and applicability. Since multimodal learning is always changing, overcoming these obstacles will help AI research and applications reach new heights.