English(EN) When Does Better Confidence Improve a Knowledge-Graph Audit?

研究论文分析了LLM知识图谱审计的置信度估计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

一篇新研究论文探讨了置信度估计在指导人工审查LLM生成的知识图谱方面的有效性。该研究将非自适应审计建模为一个有预算的覆盖问题，确定了关键统计数据并为规划设定了遗憾界限。在Re-DocRED基准上，针对各种提取配置和跨文档图的实验表明，错误率乘以扇出队列策略比联合模型留下了更多错误。然而，通用LLM验证在几种配置中显示出改进，LLM裁决有助于识别潜在的基准遗漏。 AI

影响通过改进人工干预流程，为提高LLM生成知识图谱的准确性和效率提供了见解。

排序理由研究论文发布在arXiv上，详细介绍了方法论和实验结果。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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arXiv cs.CL TIER_1 English(EN) · Zehao Lin, Kai Chen, Xixuan Hao, Erenkai Zhu, Chunyu Li, Helan Hu · 2026-11-05 05:00

更好的置信度何时能改进知识图谱审计？

arXiv:2611.05877v1 Announce Type: new Abstract: Confidence estimates guide human review in LLM-built knowledge graphs, but better error prediction can yield little improvement in an audit. We study this gap through repairability, selection headroom and label validity. Modelling n…

本文如何被排名

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更好的置信度何时能改进知识图谱审计？

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