Decomposing Uncertainty for Large Language Models through Input
  Clarification Ensembling
Bairu Hou Yujian Liu Kaizhi Qian Jacob Andreas Shiyu Chang Yang Zhang
Uncertainty decomposition refers to the task of decomposing the total
uncertainty of a model into data (aleatoric) uncertainty, resulting from the
inherent complexity or ambiguity of the data, and model (epistemic)
uncertainty, resulting from the lack of knowledge in the model. Performing
uncertainty decomposition for large language models (LLMs) is an important step
toward improving the reliability, trustworthiness, and interpretability of
LLMs, but this research task is very challenging and remains unresolved. The
existing canonical method, Bayesian Neural Network (BNN), cannot be applied to
LLMs, because BNN requires training and ensembling multiple variants of models,
which is infeasible or prohibitively expensive for LLMs. In this paper, we
introduce an uncertainty decomposition framework for LLMs, called input
clarifications ensemble, which bypasses the need to train new models. Rather
than ensembling models with different parameters, our approach generates a set
of clarifications for the input, feeds them into the fixed LLMs, and ensembles
the corresponding predictions. We show that our framework shares a symmetric
decomposition structure with BNN. Empirical evaluations demonstrate that the
proposed framework provides accurate and reliable uncertainty quantification on
various tasks. Code will be made publicly available at
https://github.com/UCSB-NLP-Chang/llm_uncertainty .