# DQI: Measuring Data Quality in NLP

arXiv: https://arxiv.org/abs/2005.00816 · HTML: https://mlsanity.com/paper/2005.00816/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2020.

## Built on by (most cited first, 24 of 24)

- [Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias](https://mlsanity.com/paper/2306.15895/) (2023)
- [NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks](https://mlsanity.com/paper/2204.05660/) (2022)
- [ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback](https://mlsanity.com/paper/2210.12329/) (2022)
- [Explaining Length Bias in LLM-Based Preference Evaluations](https://mlsanity.com/paper/2407.01085/) (2024)
- [Don't Blame the Annotator: Bias Already Starts in the Annotation Instructions](https://mlsanity.com/paper/2205.00415/) (2022)
- [Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models](https://mlsanity.com/paper/2408.02085/) (2024)
- [How Robust are Model Rankings: A Leaderboard Customization Approach for Equitable Evaluation](https://mlsanity.com/paper/2106.05532/) (2021)
- [Rainproof: An Umbrella To Shield Text Generators From Out-Of-Distribution Data](https://mlsanity.com/paper/2212.09171/) (2022)
- [ILDAE: Instance-Level Difficulty Analysis of Evaluation Data](https://mlsanity.com/paper/2203.03073/) (2022)
- [How Many Data Samples is an Additional Instruction Worth?](https://mlsanity.com/paper/2203.09161/) (2022)
- [Don't Blame the Data, Blame the Model: Understanding Noise and Bias When Learning from Subjective Annotations](https://mlsanity.com/paper/2403.04085/) (2024)
- [Towards Better Instruction Following Retrieval Models](https://mlsanity.com/paper/2505.21439/) (2025)
- [ShortcutLens: A Visual Analytics Approach for Exploring Shortcuts in Natural Language Understanding Dataset](https://mlsanity.com/paper/2208.08010/) (2022)
- [Measuring Diversity in Synthetic Datasets](https://mlsanity.com/paper/2502.08512/) (2025)
- [A Proposal to Study "Is High Quality Data All We Need?"](https://mlsanity.com/paper/2203.06404/) (2022)
- [To Find Waldo You Need Contextual Cues: Debiasing Who's Waldo](https://mlsanity.com/paper/2203.16682/) (2022)
- [Pretrained Transformers Do not Always Improve Robustness](https://mlsanity.com/paper/2210.07663/) (2022)
- [Neuron-Aware Data Selection In Instruction Tuning For Large Language Models](https://mlsanity.com/paper/2603.13201/) (2026)
- [1 Trillion Token (1TT) Platform: A Novel Framework for Efficient Data Sharing and Compensation in Large Language Models](https://mlsanity.com/paper/2409.20149/) (2024)
- [Effects of Soft-Domain Transfer and Named Entity Information on Deception Detection](https://mlsanity.com/paper/2410.14814/) (2024)
- [Autoencoder-Based Framework to Capture Vocabulary Quality in NLP](https://mlsanity.com/paper/2503.00209/) (2025)
- [HAMMER: Hamiltonian Curiosity Augmented Large Language Model Reinforcement](https://mlsanity.com/paper/2509.25240/) (2025)
- [Measuring Dataset Diversity from a Geometric Perspective](https://mlsanity.com/paper/2602.09340/) (2026)
- [PLDiv: Measuring Dataset Diversity and Semantic Collapse via Persistence Landscapes](https://mlsanity.com/paper/2610.14031/) (2026)

## Abstract (arXiv)

Neural language models have achieved human level performance across several NLP datasets. However, recent studies have shown that these models are not truly learning the desired task; rather, their high performance is attributed to overfitting using spurious biases, which suggests that the capabilities of AI systems have been over-estimated. We introduce a generic formula for Data Quality Index (DQI) to help dataset creators create datasets free of such unwanted biases. We evaluate this formula using a recently proposed approach for adversarial filtering, AFLite. We propose a new data creation paradigm using DQI to create higher quality data. The data creation paradigm consists of several data visualizations to help data creators (i) understand the quality of data and (ii) visualize the impact of the created data instance on the overall quality. It also has a couple of automation methods to (i) assist data creators and (ii) make the model more robust to adversarial attacks. We use DQI along with these automation methods to renovate biased examples in SNLI. We show that models trained on the renovated SNLI dataset generalize better to out of distribution tasks. Renovation results in reduced model performance, exposing a large gap with respect to human performance. DQI systematically helps in creating harder benchmarks using active learning. Our work takes the process of dynamic dataset creation forward, wherein datasets evolve together with the evolving state of the art, therefore serving as a means of benchmarking the true progress of AI.