Uncertainty quantification in Large Language Models (LLMs) is crucial for
applications where safety and reliability are important. In particular,
uncertainty can be used to improve the trustworthiness of LLMs by detecting
factually incorrect model responses, commonly called hallucinations.
Critically, one should seek to capture the model's semantic uncertainty, i.e.,
the uncertainty over the meanings of LLM outputs, rather than uncertainty over
lexical or syntactic variations that do not affect answer correctness. To
address this problem, we propose Kernel Language Entropy (KLE), a novel method
for uncertainty estimation in white- and black-box LLMs. KLE defines positive
semidefinite unit trace kernels to encode the semantic similarities of LLM
outputs and quantifies uncertainty using the von Neumann entropy. It considers
pairwise semantic dependencies between answers (or semantic clusters),
providing more fine-grained uncertainty estimates than previous methods based
on hard clustering of answers. We theoretically prove that KLE generalizes the
previous state-of-the-art method called semantic entropy and empirically
demonstrate that it improves uncertainty quantification performance across
multiple natural language generation datasets and LLM architectures.

在大型语言模型中，不确定性量化对于关系到安全性和可靠性的应用至关重要。我们提出了 Kernel Language Entropy（KLE），这是一种用于估计白盒和黑盒语言模型中不确定性的新方法，它能够捕捉到模型输出的语义不确定性，并通过 von Neumann 熵来量化不确定性。实验证明，KLE 在多个自然语言生成数据集和语言模型架构上提供了更好的不确定性量化性能。