The Large language models (LLMs) have showcased superior capabilities in sophisticated tasks across various domains, stemming from basic question-answer (QA), they are nowadays used as decision assistants or explainers for unfamiliar content. However, they are not always correct due to the data sparsity in specific domain corpus, or the model's hallucination problems. Given this, how much should we trust the responses from LLMs? This paper presents a novel way to evaluate the uncertainty that captures the directional instability, by constructing a directional graph from entailment probabilities, and we innovatively conduct Random Walk Laplacian given the asymmetric property of a constructed directed graph, then the uncertainty is aggregated by the derived eigenvalues from the Laplacian process. We also provide a way to incorporate the existing work's semantics uncertainty with our proposed layer. Besides, this paper identifies the vagueness issues in the raw response set and proposes an augmentation approach to mitigate such a problem, we conducted extensive empirical experiments and demonstrated the superiority of our proposed solutions.

该论文提出了一种评估大型语言模型不确定性的新方法，通过构建由蕴含概率组成的方向图，并创新地进行随机行走拉普拉斯过程以捕捉方向不稳定性，然后通过拉普拉斯过程得到的特征值来聚合不确定性。此外，该论文识别了原始回应集中的模糊问题，并提出了一种补充方法以缓解此问题，我们进行了大量实证实验并证明了我们提出的解决方案的优越性。

基于定向蕴涵图和主张级响应增强的LLM不确定性量化