LlamaIndex 用置信度分数决定文档提取自动化阈值

predicting model uncertainty is a hard problem. perfectly "calibrated" confidence scores are exact ...

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! llamaindex.ai/blog/what-make… Your browser does not support the video tag. 🔗 View on Twitter LlamaIndex 🦙 @llama_index confidence scores only matter if they help you decide what to automate. for document extraction, that usually means knowing how much work you can safely accept at a given precision target. in our latest post, we look at confidence scoring through that lens, including: ✅️ confidence cutoffs ✅️ precision vs. recall ✅️ score coverage ✅️ score granularity ✅️ human review volume using ExtractBench, we compare how different extraction systems perform after confidence filtering. at a 97% precision target, LlamaParse Agentic Plus reached 66.48% recall on expected fields after filtering. the useful part of a confidence score isn’t the number itself. it’s whether you can use it to control automation and review in production. 👉️ read the full post: llamaindex.ai/blog/what-make… f Your browser does not support the video tag. 🔗 View on Twitter 🔗 View Quoted Tweet 💬 5 🔄 0 ❤️ 1 👀 1088 📊 5 ⚡