# CrossCheck: Rapid, Reproducible, and Interpretable Model Evaluation

arXiv: https://arxiv.org/abs/2004.07993 · HTML: https://mlsanity.com/paper/2004.07993/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2020.

## Built on by (most cited first, 7 of 7)

- [Robustness Gym: Unifying the NLP Evaluation Landscape](https://mlsanity.com/paper/2101.04840/) (2021)
- [LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools and Self-Explanations](https://mlsanity.com/paper/2401.12576/) (2024)
- [Automatic Error Analysis for Document-level Information Extraction](https://mlsanity.com/paper/2209.07442/) (2022)
- [Azimuth: Systematic Error Analysis for Text Classification](https://mlsanity.com/paper/2212.08216/) (2022)
- [iSEA: An Interactive Pipeline for Semantic Error Analysis of NLP Models](https://mlsanity.com/paper/2203.04408/) (2022)
- [IMRNNs: An Efficient Method for Interpretable Dense Retrieval via Embedding Modulation](https://mlsanity.com/paper/2601.20084/) (2026)
- [TRIVEA: Transparent Ranking Interpretation using Visual Explanation of Black-Box Algorithmic Rankers](https://mlsanity.com/paper/2308.14622/) (2023)

## Abstract (arXiv)

Evaluation beyond aggregate performance metrics, e.g. F1-score, is crucial to both establish an appropriate level of trust in machine learning models and identify future model improvements. In this paper we demonstrate CrossCheck, an interactive visualization tool for rapid crossmodel comparison and reproducible error analysis. We describe the tool and discuss design and implementation details. We then present three use cases (named entity recognition, reading comprehension, and clickbait detection) that show the benefits of using the tool for model evaluation. CrossCheck allows data scientists to make informed decisions to choose between multiple models, identify when the models are correct and for which examples, investigate whether the models are making the same mistakes as humans, evaluate models' generalizability and highlight models' limitations, strengths and weaknesses. Furthermore, CrossCheck is implemented as a Jupyter widget, which allows rapid and convenient integration into data scientists' model development workflows.