用预训练模型计算语义不确定性的新方法，结果更可靠。
A statistically consistent measure of semantic uncertainty using Language Models
- 基于语言模型输出构造语义谱熵，无需内部生成过程
- 模拟实验显示对生成随机性有强鲁棒性，估计准确
- 适合评估大模型输出可信度，尤其关注不确定性
为解决语言模型输出不确定性量化难题，我们提出一种新的语义不确定性度量——语义谱熵，在弱假设下具有统计一致性。该方法通过简单算法实现，仅依赖标准预训练语言模型，无需访问内部生成过程。对不同架构和设置均适用，约束极少。通过全面模拟研究证明，该方法即使在生成模型固有的随机性下，仍能提供准确且稳健的语义不确定性估计。
原文摘要 · Abstract (English)
To address the challenge of quantifying uncertainty in the outputs generated by language models, we propose a novel measure of semantic uncertainty, semantic spectral entropy, that is statistically consistent under mild assumptions. This measure is implemented through a straightforward algorithm that relies solely on standard, pretrained language models, without requiring access to the internal generation process. Our approach imposes minimal constraints on the choice of language models, making it broadly applicable across different architectures and settings. Through comprehensive simulation studies, we demonstrate that the proposed method yields an accurate and robust estimate of semantic uncertainty, even in the presence of the inherent randomness characteristic of generative language model outputs.
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