Argo: Efficient Importance Labeling for Enterprise Email Systems

2025-12-08 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Software Development & Engineering · Depth: Expert, extended

Summary

Argo is an enterprise-email labeling framework designed to provide cost-efficient, context-aware email importance labeling at scale, addressing the prohibitive costs of using large language models like GPT-4.1. The system achieves near-GPT-level labeling quality while significantly reducing operational expenses. Argo incorporates an efficient profiler that navigates the vast cost-quality tradeoff space, identifying optimal labeling alternatives by leveraging email label characteristics (e.g., binary vs. non-binary decisions). It also features an on-demand resource provisioning scheme to intelligently scale with real-time email loads, minimizing cost increases during peak inference. Across three open-source email datasets—Enron, Fauci, and Hillary Clinton—Argo demonstrated 148-167x inference cost reduction with negligible quality degradation, 20-640000x lower profiling costs, and 2.2-3.8x lower cost increases under load.

Key takeaway

For MLOps Engineers or AI Architects tasked with deploying enterprise-scale email importance labeling, you should evaluate hybrid frameworks like Argo. This system demonstrates how to achieve near-GPT-level labeling quality with 148-167x lower inference costs and 20-640000x cheaper profiling. By intelligently combining SLM cascades and embedding classifiers, and optimizing resource provisioning, you can overcome the prohibitive costs of direct LLM usage without sacrificing accuracy, making advanced email management practical.

Key insights

Argo efficiently labels enterprise emails by intelligently balancing LLM-level quality with significantly reduced computational costs.

Principles

• Email label characteristics (e.g., binary vs. non-binary) guide optimal labeling method selection.
• SLM cascades should prioritize cheaper models, escalating to more expensive ones only when confidence is low.
• Profiling costs are minimized by exploiting knob independence and incrementally building calibration sets.

Method

Argo's profiler uses a calibration set, label requirements, SLMs, and embedding models to identify Pareto-efficient knob values. It assigns SLM cascades for non-binary labels and embedding classifiers for binary ones, then applies a greedy resource provisioning algorithm for SLM cascades.

In practice

• Employ SLM cascades for multi-class email labels and embedding classifiers for binary decisions.
• Order SLMs in cascades by increasing model size to minimize inference costs.
• Utilize a greedy algorithm for SLM provisioning to manage peak email load efficiently.

Topics

• Email Importance Labeling
• Large Language Models
• Small Language Models
• Cost Efficiency
• Resource Provisioning
• Model Cascades

Code references

• Lightning-AI/pytorch-lightning

Best for: AI Engineer, NLP Engineer, CTO, Machine Learning Engineer, MLOps Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.