arXiv:2606.31446v1 Announce Type: new Abstract: RVL-CDIP is a popular dataset for benchmarking document classifiers. However, the dataset contains ample amounts of label errors as well as non-trivial amounts of test-train overlap, both of which may impact model performance metrics. In this paper, we address these two problems by (1) finding and fixing label errors, and (2) detecting and addressing test-train overlap. We produce several variations of RVL-CDIP with label error and test-train overlap fixes, and benchmark document classification performance on these new RVL-CDIP variations. Our ri
The proliferation of AI models across various tasks highlights the increasing importance of high-quality, reliable datasets for accurate benchmarking and development.
Improving benchmarks like RVL-CDIP directly leads to more trustworthy model performance metrics, which is crucial for evaluating and deploying AI systems effectively.
The ability to accurately compare and assess document classification models will improve, reducing the risk of overestimating or underestimating model capabilities due to data flaws.
- · AI researchers
- · Model developers
- · Data quality tools
- · Researchers relying on flawed benchmarks
- · Undisciplined data collection practices
Refined RVL-CDIP datasets will provide more accurate comparative results for document classification models.
Improved benchmarking practices may lead to a broader re-evaluation of performance claims in other AI subfields.
A higher standard for dataset quality could accelerate development in areas that benefit from robust, error-free training data.
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