  Modern neural network libraries all take as a hyperparameter a random seed,
typically used to determine the initial state of the model parameters. This
opinion piece argues that there are some safe uses for random seeds: as part of
the hyperparameter search to select a good model, creating an ensemble of
several models, or measuring the sensitivity of the training algorithm to the
random seed hyperparameter. It argues that some uses for random seeds are
risky: using a fixed random seed for "replicability" and varying only the
random seed to create score distributions for performance comparison. An
analysis of 85 recent publications from the ACL Anthology finds that more than
50% contain risky uses of random seeds.
