Jerry Liu：预测模型不确定性是一个难题
预测模型不确定性是一个难题。
完美“校准”的置信度分数是输出是否正确的精确概率值。
这对于智能体式决策极其重要，包括文档提取。我在下面制作了一个很棒的视频，展示如何使用置信度分数来选择决策阈值并调整精确度/召回率。
如果你设置一个非常高的阈值，那么自动化处理的就更少，但更多自动化提取的内容是正确的。如果你设置一个低阈值，那么自动化处理的就更多，但提取中会出现更多错误。
看看我们的博客吧！
https://www.llamaindex.ai/blog/what-makes-an-extraction-confidence-score-useful
对照原文
predicting model uncertainty is a hard problem. perfectly "calibrated" confidence scores are exact probability values on whether the output is correct. this is extremely important for agentic decision making, including document extraction. i made a sick video below showing how confidence scores can be used to choose decision thresholds and vary precision / recall. if you set a really high threshold, then you automate less, but more of the automated extraction is correct. if you set a low threshold, then you automate more, but there's more errors in the extraction. check out our blog! https://t.co/Mel0ScoCBv