  Language models are known to produce vague and generic outputs. We propose
two unsupervised decoding strategies based on either word-frequency or
point-wise mutual information to increase the specificity of any model that
outputs a probability distribution over its vocabulary at generation time. We
test the strategies in a prompt completion task; with human evaluations, we
find that both strategies increase the specificity of outputs with only modest
decreases in sensibility. We also briefly present a summarization use case,
where these strategies can produce more specific summaries.

Computation and Language