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Large-scale datasets in the real world inevitably involve label noise. Deepmodels can gradually overfit noisy labels and thus degrade modelgeneralization. To mitigate the effects of label noise, learning with noisylabels (LNL) methods are designed to achieve better generalization performance.Due to the lack of suitable datasets, previous studies have frequently employedsynthetic label noise to mimic real-world label noise. However, synthetic noiseis not instance-dependent, making this approximation not always effective inpractice. Recent research has proposed benchmarks for learning with real-worldnoisy labels. However, the noise sources within may be single or fuzzy, makingbenchmarks different from data with heterogeneous label noises in the realworld. To tackle these issues, we contribute NoisywikiHow, the largest NLPbenchmark built with minimal supervision. Specifically, inspired by humancognition, we explicitly construct multiple sources of label noise to imitatehuman errors throughout the annotation, replicating real-world noise, whosecorruption is affected by both ground-truth labels and instances. Moreover, weprovide a variety of noise levels to support controlled experiments on noisydata, enabling us to evaluate LNL methods systematically and comprehensively.After that, we conduct extensive multi-dimensional experiments on a broad rangeof LNL methods, obtaining new and intriguing findings.

NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language Processing