PAC Neural Prediction Set Learning to Quantify the Uncertainty of
  Generative Language Models
Sangdon Park Taesoo Kim
Uncertainty learning and quantification of models are crucial tasks to
enhance the trustworthiness of the models. Importantly, the recent surge of
generative language models (GLMs) emphasizes the need for reliable uncertainty
quantification due to the concerns on generating hallucinated facts. In this
paper, we propose to learn neural prediction set models that comes with the
probably approximately correct (PAC) guarantee for quantifying the uncertainty
of GLMs. Unlike existing prediction set models, which are parameterized by a
scalar value, we propose to parameterize prediction sets via neural networks,
which achieves more precise uncertainty quantification but still satisfies the
PAC guarantee. We demonstrate the efficacy of our method on four types of
language datasets and six types of models by showing that our method improves
the quantified uncertainty by $63\%$ on average, compared to a standard
baseline method.