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In Natural Language Generation (NLG) tasks, for any input, multiplecommunicative goals are plausible, and any goal can be put into words, orproduced, in multiple ways. We characterise the extent to which humanproduction varies lexically, syntactically, and semantically across four NLGtasks, connecting human production variability to aleatoric or datauncertainty. We then inspect the space of output strings shaped by a generationsystem's predicted probability distribution and decoding algorithm to probe itsuncertainty. For each test input, we measure the generator's calibration tohuman production variability. Following this instance-level approach, weanalyse NLG models and decoding strategies, demonstrating that probing agenerator with multiple samples and, when possible, multiple references,provides the level of detail necessary to gain understanding of a model'srepresentation of uncertainty. Code available athttps://github.com/dmg-illc/nlg-uncertainty-probes.

What Comes Next? Evaluating Uncertainty in Neural Text Generators Against Human Production Variability