Token-Free Language Models for Efficient Telecom Log Analysis
Dr. Chen Li, Dr. Marco Fiore
IMDEA Networks / NEC Laboratories Europe
Abstract
Traditional LLMs struggle with telecom network logs due to their technical vocabulary and structured format not aligning well with standard tokenization. We propose a byte-level token-free language model specifically designed for telecom log analysis. Our model processes raw byte sequences directly, avoiding out-of-vocabulary issues common with standard tokenizers on network log data. On a benchmark of 1M real operator logs, our approach achieves 91% fault classification accuracy and generates root cause explanations that experts rate as helpful 85% of the time.
AI Summary
- Byte-level token-free LM designed specifically for telecom log analysis.
- 91% fault classification accuracy on 1M real operator logs.
- Root cause explanations rated helpful 85% of the time by experts.
- Avoids OOV issues common with standard tokenizers on network data.
Key Findings
- 1Byte-level processing handles diverse log formats without preprocessing.
- 2The model learns meaningful representations of network protocol structures.
- 3Fine-tuning on operator-specific logs improves accuracy by 8% over generic model.
Industry Implications
Enables automated root cause analysis for faster network troubleshooting.
Reduces mean time to repair for network faults.
Applicable to multi-vendor environments with heterogeneous log formats.
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