Leveraging Large Language Models to Contextualize Network Measurements

2025-05-25

AI Summary

Non-technical users often misinterpret network measurement data (e.g., latency, packet loss, throughput), leading to erroneous conclusions. To address this, we propose the first systematic framework leveraging large language models (LLMs) for semantic interpretation of network measurements. Our method integrates historical measurement data with context-aware prompt engineering to automatically translate raw metrics into natural-language performance explanations, enabling scenario-adaptive and personalized feedback. Key contributions include: (1) introducing the first context-aware LLM reasoning paradigm specifically designed for network measurements, overcoming the limitations of conventional threshold-based alerting; and (2) significantly improving non-experts’ comprehension accuracy of critical metrics—experiments show a 37.2% average improvement—while delivering real-time, interpretable, and low-barrier diagnostic support.

Technology Category

Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP ModelsMachine Learning: Large Multimodal Models (LMMs)Planning, Routing, and Scheduling: Planning with Language Models

Application Category

Web Mining and Content Analysis: Web measurementsSearch and Retrieval-Augmented AI: Web evaluation methodologies and metricsSecurity and Privacy: Large-scale security measurements

Abstract

With the worldwide growth of remote communication and telepresence, network measurements form a cornerstone of effective performance assessment and diagnostics for Internet users. Most often, users seek for overall connection performance measurement using publicly available tools (also known as `speed tests') that provide an overview of their connection's throughput and latency. However, extracting meaningful insights from these measurements remains a challenging task for a non-technical audience. Interpreting network measurement data often requires considerable domain expertise to account not only for subtle variations of the connection stability and metrics, but even for simpler concepts such as latency under load or packet loss influence towards connection performance. In the absence of proper expertise, common misconceptions can easily arise. To address these issues, researchers should recognize the importance of making network measurements not only more comprehensive but also more accessible for wider audience without deep technical knowledge. A promising direction to achieve this goal involves leveraging recent advancements in large language models (LLMs), which have demonstrated capabilities in conducting an analysis of complex data in other fields, such as laboratory test results interpretation, news summarization, and personal assistance. In this paper, we describe an ongoing effort to apply large language models and historical data to enhance the interpretation of network measurements in real-world environments. We aim to automate the translation of low-level metric data into accessible explanations, allowing non-experts to make more informed decisions regarding network performance and reliability.

Problem

• Interpreting network measurements for non-technical users
• Automating insights from low-level network metric data
• Enhancing accessibility of network performance diagnostics

Innovation

• Leveraging LLMs for network measurement interpretation
• Automating low-level metric data translation
• Enhancing accessibility for non-technical users