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Motivated by the goals of dataset pruning and defect identification, agrowing body of methods have been developed to score individual examples withina dataset. These methods, which we call "example difficulty scores", aretypically used to rank or categorize examples, but the consistency of rankingsbetween different training runs, scoring methods, and model architectures isgenerally unknown. To determine how example rankings vary due to these randomand controlled effects, we systematically compare different formulations ofscores over a range of runs and model architectures. We find that scoreslargely share the following traits: they are noisy over individual runs of amodel, strongly correlated with a single notion of difficulty, and revealexamples that range from being highly sensitive to insensitive to the inductivebiases of certain model architectures. Drawing from statistical genetics, wedevelop a simple method for fingerprinting model architectures using a fewsensitive examples. These findings guide practitioners in maximizing theconsistency of their scores (e.g. by choosing appropriate scoring methods,number of runs, and subsets of examples), and establishes comprehensivebaselines for evaluating scores in the future.

Dataset Difficulty and the Role of Inductive Bias