---
id: 20260808-T0-10
title: "PPDL：将LLM工作流转化为概率编程，让AI置信度可量化"
title_en: "PPDL: LLM Workflows as Probabilistic Programs, Making AI Confidence Quantifiable"
url: https://ai.daily.yangsir.net/daily/20260808-T0-10
issue_date: 2026-08-08
publish_date: 2026-08-07T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2608.05234
---

# PPDL：将LLM工作流转化为概率编程，让AI置信度可量化

PPDL框架将基于LLM的应用流程建模为概率程序，使开发者能够获得每次输出的置信度指标，而不仅仅是文本结果。该方法通过概率推理来捕捉LLM输出的不确定性，为构建可靠的LLM应用提供了新的技术路径。对开发者而言，这意味着不再需要盲目信任模型输出，而是可以在关键决策点上依赖置信度数据进行判断与容错设计。

## English Version

**PPDL: LLM Workflows as Probabilistic Programs, Making AI Confidence Quantifiable**

PPDL is a framework that models LLM-based application flows as probabilistic programs, giving developers confidence metrics for each output. This approach uses probabilistic inference to capture output uncertainty, providing a new technical path for building reliable LLM applications. Developers no longer need to blindly trust model outputs but can rely on confidence data for decision-making and fault tolerance design.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2608.05234)

**详情页**：https://ai.daily.yangsir.net/daily/20260808-T0-10

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