---
id: 20260822-T0-13
title: "黑盒LLM置信度估计新方法：更精准的不确定性量化"
title_en: "Improved Confidence Estimates for Black-Box LLMs"
url: https://ai.daily.yangsir.net/daily/20260822-T0-13
issue_date: 2026-08-22
publish_date: 2026-08-21T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2608.19323
---

# 黑盒LLM置信度估计新方法：更精准的不确定性量化

一篇新论文提出一种改进黑盒大模型置信度估计的方法。现有方法如口头置信度或多次生成，多为零样本（zero-shot）方式，且对不确定性量化不够准确。新方法通过迭代生成来提升置信度评分的可靠性，让用户能更好地判断模型输出的可信程度。这对于大模型的安全部署具有实际意义。

## English Version

**Improved Confidence Estimates for Black-Box LLMs**

A new paper introduces an improved method for estimating confidence in black-box LLMs. Current methods, like verbalized confidence or multiple generations, are often zero-shot and lack accurate uncertainty quantification. The new approach refines confidence scores through iterative generations, providing users with a better gauge of output reliability—a practical step for safer LLM deployment.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2608.19323)

**详情页**：https://ai.daily.yangsir.net/daily/20260822-T0-13

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