# The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective

arXiv: https://arxiv.org/abs/2407.08583 · HTML: https://mlsanity.com/paper/2407.08583/

## Key facts

- Cited by 15 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 258 of its 296 references that are in the map.
- First posted on arXiv 11 Jul 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 258)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs](https://mlsanity.com/paper/2111.02114/) (2021)
- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [ImageBind: One Embedding Space To Bind Them All](https://mlsanity.com/paper/2305.05665/) (2023)
- [MVBench: A Comprehensive Multi-modal Video Understanding Benchmark](https://mlsanity.com/paper/2311.17005/) (2023)
- [LIMA: Less Is More for Alignment](https://mlsanity.com/paper/2305.11206/) (2023)
- [Kosmos-2: Grounding Multimodal Large Language Models to the World](https://mlsanity.com/paper/2306.14824/) (2023)

## Built on by (most cited first, 15 of 15)

- [Img-Diff: Contrastive Data Synthesis for Multimodal Large Language Models](https://mlsanity.com/paper/2408.04594/) (2024)
- [Explainable Action Form Assessment by Exploiting Multimodal Chain-of-Thoughts Reasoning](https://mlsanity.com/paper/2512.15153/) (2025)
- [Diversity as a Reward: Fine-Tuning LLMs on a Mixture of Domain-Undetermined Data](https://mlsanity.com/paper/2502.04380/) (2025)
- [Data-Juicer 2.0: Cloud-Scale Adaptive Data Processing for and with Foundation Models](https://mlsanity.com/paper/2501.14755/) (2024)
- [HumanVBench: Probing Human-Centric Video Understanding in MLLMs with Automatically Synthesized Benchmarks](https://mlsanity.com/paper/2412.17574/) (2024)
- [Data-Juicer Sandbox: A Feedback-Driven Suite for Multimodal Data-Model Co-development](https://mlsanity.com/paper/2407.11784/) (2024)
- [Federated Data-Efficient Instruction Tuning for Large Language Models](https://mlsanity.com/paper/2410.10926/) (2024)
- [Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs](https://mlsanity.com/paper/2601.17058/) (2026)
- [MindGYM: What Matters in Question Synthesis for Thinking-Centric Fine-Tuning?](https://mlsanity.com/paper/2503.09499/) (2025)
- [DetailMaster: Can Your Text-to-Image Model Handle Long Prompts?](https://mlsanity.com/paper/2505.16915/) (2025)
- [A Survey of LLM × DATA](https://mlsanity.com/paper/2505.18458/) (2025)
- [Learning from Reasoning Failures via Synthetic Data Generation](https://mlsanity.com/paper/2504.14523/) (2025)
- [MAJORScore: A Novel Metric for Evaluating Multimodal Relevance via Joint Representation](https://mlsanity.com/paper/2509.21365/) (2025)
- [Near-Oracle KV Selection via Pre-hoc Sparsity for Long-Context Inference](https://mlsanity.com/paper/2602.08329/) (2026)
- [X-AVDD: Cross-Attentive Audio-Visual Dataset Distillation](https://mlsanity.com/paper/2610.13687/) (2026)

## Abstract (arXiv)

The rapid development of large language models (LLMs) has been witnessed in recent years. Based on the powerful LLMs, multi-modal LLMs (MLLMs) extend the modality from text to a broader spectrum of domains, attracting widespread attention due to the broader range of application scenarios. As LLMs and MLLMs rely on vast amounts of model parameters and data to achieve emergent capabilities, the importance of data is receiving increasingly widespread attention and recognition. Tracing and analyzing recent data-oriented works for MLLMs, we find that the development of models and data is not two separate paths but rather interconnected. On the one hand, vaster and higher-quality data contribute to better performance of MLLMs; on the other hand, MLLMs can facilitate the development of data. The co-development of multi-modal data and MLLMs requires a clear view of 1) at which development stages of MLLMs specific data-centric approaches can be employed to enhance certain MLLM capabilities, and 2) how MLLMs, utilizing those capabilities, can contribute to multi-modal data in specific roles. To promote the data-model co-development for MLLM community, we systematically review existing works related to MLLMs from the data-model co-development perspective. A regularly maintained project associated with this survey is accessible at https://github.com/modelscope/data-juicer/blob/main/docs/awesome_llm_data.md.