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