  Today's probabilistic language generators fall short when it comes to
producing coherent and fluent text despite the fact that the underlying models
perform well under standard metrics, e.g., perplexity. This discrepancy has
puzzled the language generation community for the last few years. In this work,
we posit that the abstraction of natural language generation as a discrete
stochastic process--which allows for an information-theoretic analysis--can
provide new insights into the behavior of probabilistic language generators,
e.g., why high-probability texts can be dull or repetitive. Humans use language
as a means of communicating information, aiming to do so in a simultaneously
efficient and error-minimizing manner; in fact, psycholinguistics research
suggests humans choose each word in a string with this subconscious goal in
mind. We formally define the set of strings that meet this criterion: those for
which each word has an information content close to the expected information
content, i.e., the conditional entropy of our model. We then propose a simple
and efficient procedure for enforcing this criterion when generating from
probabilistic models, which we call locally typical sampling. Automatic and
human evaluations show that, in comparison to nucleus and top-k sampling,
locally typical sampling offers competitive performance (in both abstractive
summarization and story generation) in terms of quality while consistently
reducing degenerate repetitions.
