# Model-Based Data-Centric AI: Bridging the Divide Between Academic Ideals and Industrial Pragmatism

arXiv: https://arxiv.org/abs/2403.01832 · HTML: https://mlsanity.com/paper/2403.01832/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 18 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 11 of 11)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [DocVQA: A Dataset for VQA on Document Images](https://mlsanity.com/paper/2007.00398/) (2020)
- [Self-Rewarding Language Models](https://mlsanity.com/paper/2401.10020/) (2024)
- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [Data Quality Toolkit: Automatic assessment of data quality and remediation for machine learning datasets](https://mlsanity.com/paper/2108.05935/) (2021)
- [DMOps: Data Management Operation and Recipes](https://mlsanity.com/paper/2301.01228/) (2023)
- [Inter-Annotator Agreement in the Wild: Uncovering Its Emerging Roles and Considerations in Real-World Scenarios](https://mlsanity.com/paper/2306.14373/) (2023)
- [Improving Scene Text Recognition for Character-Level Long-Tailed Distribution](https://mlsanity.com/paper/2304.08592/) (2023)
- [Curator: Creating Large-Scale Curated Labelled Datasets using Self-Supervised Learning](https://mlsanity.com/paper/2212.14099/) (2022)
- [Transcending Traditional Boundaries: Leveraging Inter-Annotator Agreement (IAA) for Enhancing Data Management Operations (DMOps)](https://mlsanity.com/paper/2306.14374/) (2023)

## Built on by (most cited first, 1 of 1)

- [Optimizing Class Distributions for Bias-Aware Multi-Class Learning](https://mlsanity.com/paper/2509.11588/) (2025)

## Abstract (arXiv)

This paper delves into the contrasting roles of data within academic and industrial spheres, highlighting the divergence between Data-Centric AI and Model-Agnostic AI approaches. We argue that while Data-Centric AI focuses on the primacy of high-quality data for model performance, Model-Agnostic AI prioritizes algorithmic flexibility, often at the expense of data quality considerations. This distinction reveals that academic standards for data quality frequently do not meet the rigorous demands of industrial applications, leading to potential pitfalls in deploying academic models in real-world settings. Through a comprehensive analysis, we address these disparities, presenting both the challenges they pose and strategies for bridging the gap. Furthermore, we propose a novel paradigm: Model-Based Data-Centric AI, which aims to reconcile these differences by integrating model considerations into data optimization processes. This approach underscores the necessity for evolving data requirements that are sensitive to the nuances of both academic research and industrial deployment. By exploring these discrepancies, we aim to foster a more nuanced understanding of data's role in AI development and encourage a convergence of academic and industrial standards to enhance AI's real-world applicability.