TeleCom-Bench: How Far Are Large Language Models from Telecom Industrial Applications?

The AI Cloud Team of ZTE released the TeleCom-Bench benchmark in May 2026, which includes 22,678 samples. It systematically evaluates LLMs' capabilities in knowledge understanding and end-to-end workflow applications in the telecom field. It reveals the "execution gap" phenomenon: the model achieves an accuracy rate of about 90% in language interface tasks (e.g., intent recognition), while dropping sharply to about 30% in procedural execution tasks (e.g., solution generation). This provides key references for the development of LLMs in telecom industrial applications.

Existing telecom-related benchmarks have four major shortcomings: 1. Focus on static knowledge (e.g., communication principles); 2. Ignore device specificity (vendor equipment operation specifications); 3. Lack end-to-end workflow evaluation (isolated atomic skills); 4. Detached from production environments (simplified scenarios). TeleCom-Bench aims to fill these gaps and provide an evaluation framework that is close to industrial practical needs.

TeleCom-Bench includes 12 evaluation sets with a total of 22,678 samples, based on a collaborative hierarchical structure:

1. Multi-dimensional knowledge understanding layer: Evaluates telecom basics, 3GPP protocols, 5G architecture, and proprietary product knowledge. Samples are generated using knowledge graphs to ensure accuracy;
2. End-to-end knowledge application layer: Covers six tasks including intent recognition, entity extraction, event verification, tool calling, root cause analysis, and solution generation, built based on real network agent workflow trajectories.

Evaluation of 8 mainstream LLMs found:

• Language interface tasks (intent recognition, entity extraction): ~90% accuracy;
• Procedural execution tasks (e.g., solution generation): ~30% accuracy; The gap indicates that current LLMs can be competent as "diagnosticians" (understanding problems and analyzing causes), but not as "field engineers" (formulating and executing complete solutions).

TeleCom-Bench reveals that current LLMs perform well in knowledge understanding in the telecom field, but there is a huge gap in procedural execution. This conclusion also has reference value for vertical fields that require complex execution, such as manufacturing and energy.

1. Precisely locate capability gaps: Use TeleCom-Bench to guide alignment training of domain-specific models;
2. Strengthen procedural execution capabilities: Specialized training on standard operating procedures, tool calling sequences, and operation dependencies;
3. Adopt human-machine collaboration mode: Models are responsible for knowledge understanding and preliminary analysis, while human engineers review solutions and make decisions.

• Features of evaluation methodology: Knowledge graph-driven sample generation, task construction based on real trajectories, multi-dimensional scoring (results + intermediate steps);
• Open-source contributions: The dataset and evaluation code have been open-sourced (GitHub address: https://github.com/ZTE-AICloud/TeleCom-Bench), providing public resources for telecom LLM research.