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English(EN) Argo: Efficient Importance Labeling for Enterprise Email Systems

Argo框架通过LLM替代方案降低企业电子邮件标注成本

研究人员开发了Argo,一个旨在使大规模、上下文感知电子邮件标注在企业中变得实用的新框架。Argo通过探索替代标注方案,而不是仅仅依赖GPT-4.1等昂贵的LLM,以显著更低的成本实现接近GPT级别的标注质量。该系统包括一个分析器,用于识别成本效益高的标注替代方案,以及一个按需配置方案,以智能地适应实时负载。在三个开源数据集上,Argo展示了显著的推理成本降低,而质量损失可忽略不计。 AI

影响 Argo为企业利用先进AI进行电子邮件组织提供了一个经济高效的解决方案,有可能提高生产力。

排序理由 该集群描述了一个新框架和相关的研究论文,详细介绍了其方法和性能。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.MA (Multiagent) 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

Argo框架通过LLM替代方案降低企业电子邮件标注成本

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该集群描述了一个新框架和相关的研究论文,详细介绍了其方法和性能。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Junchen Jiang ·

    Argo:企业电子邮件系统的高效重要性标注

    Email importance labeling has long been a critical yet challenging problem for businesses and individuals. Traditional approaches; such as keyword matching, user-defined rules, and sender-based heuristics; demand extensive manual feature engineering and fail to scale effectively …