阿斯塔纳时间
AI QAZAQSTANKZ · AI测试版

哈萨克斯坦人工智能与高科技领域的智能门户

Research1 分钟阅读

内容来自原始来源:

arXiv — cs.AI

在 arXiv — cs.AI 阅读全文

本内容来自公开来源。若发布内容影响您的权利 → 申请删除信息

标题为英语From High Recall to High Utility: Dataset-Adaptive Post-Processing of LLM-Generated Customer Intents

Large language models can extract useful signals from heterogeneous enterprise data, but high-recall extraction often produces outputs that are duplicated, uneven in granularity, semantically overlapping, or too numerous for downstream systems and human reviewers to use…

arXiv — cs.AI作者: Mahesh Viswanathan, Joan Rossello, Leticia Fernandes, Paul Mutawe2026年10月8日 (10月8日)
提供 1 种语言版本

AI 摘要:要点

  • Large language models can extract useful signals from heterogeneous enterprise data, but high-recall extraction often produces outputs that are duplicated, uneven in granularity, semantically overlapping, or too numerous for downstream systems and human reviewers to use…
Research
KZ·AI 8039
Инсайт дня

From High Recall to High Utility: Dataset-Adaptive Post-Processing of LLM-Generated Customer Intents

标签:#arXiv
Поделиться:

相关文章

返回列表