# Fix your Models by Fixing your Datasets

arXiv: https://arxiv.org/abs/2112.07844 · HTML: https://mlsanity.com/paper/2112.07844/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 7 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics](https://mlsanity.com/paper/2009.10795/) (2020)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [Dynamic Data Selection and Weighting for Iterative Back-Translation](https://mlsanity.com/paper/2004.03672/) (2020)
- [Managing ML Pipelines: Feature Stores and the Coming Wave of Embedding Ecosystems](https://mlsanity.com/paper/2108.05053/) (2021)

## Built on by (most cited first, 3 of 3)

- [reStructured Pre-training](https://mlsanity.com/paper/2206.11147/) (2022)
- [A Data-centric Framework for Improving Domain-specific Machine Reading Comprehension Datasets](https://mlsanity.com/paper/2304.00483/) (2023)
- [Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability](https://mlsanity.com/paper/2507.02922/) (2025)

## Abstract (arXiv)

The quality of underlying training data is very crucial for building performant machine learning models with wider generalizabilty. However, current machine learning (ML) tools lack streamlined processes for improving the data quality. So, getting data quality insights and iteratively pruning the errors to obtain a dataset which is most representative of downstream use cases is still an ad-hoc manual process. Our work addresses this data tooling gap, required to build improved ML workflows purely through data-centric techniques. More specifically, we introduce a systematic framework for (1) finding noisy or mislabelled samples in the dataset and, (2) identifying the most informative samples, which when included in training would provide maximal model performance lift. We demonstrate the efficacy of our framework on public as well as private enterprise datasets of two Fortune 500 companies, and are confident this work will form the basis for ML teams to perform more intelligent data discovery and pruning.