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Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings

💡 原文英文，约100词，阅读约需1分钟。
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内容提要

本文提出了一种利用语义嵌入改进大型语言模型不确定性量化的方法，能够更平滑、稳健地估计语义不确定性，降低无关词引入的偏差，并显著减少计算开销。研究表明该方法在准确性和细致度上优于传统方法。

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标签

models 不确定性量化 准确性 大型语言模型 计算开销 语义嵌入
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