TeleCom-Bench reveals LLMs reach 90% on telecom intent and entity tasks but drop to 30% on solution generation and root cause analysis in live network scenarios.
2406.01768 , archivePrefix=
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
representative citing papers
A vision-language LLM forecasts HAP attitudes from telemetry for proactive beamforming, achieving 22.1% higher user service ratio and 12.5% higher sum-rate than baselines in simulations with mean latency of 36 ms.
MM-Telco creates multimodal benchmarks for telecom and demonstrates that fine-tuned LLMs and VLMs achieve significant performance gains on domain-specific tasks.
Argues that wireless data's configuration dependence and lack of self-containment make monolithic foundation models unsuitable for AI-native 6G, favoring instead composable agentic architectures.
LoRA continued pretraining on a small U.S. transportation corpus lifts BLEU-4 and ROUGE for Qwen2.5-7B and LLaMA-3.1-8B far above the other four models tested.
citing papers explorer
-
TeleCom-Bench: How Far Are Large Language Models from Industrial Telecommunication Applications?
TeleCom-Bench reveals LLMs reach 90% on telecom intent and entity tasks but drop to 30% on solution generation and root cause analysis in live network scenarios.
-
Multimodal Large Language Model Enabled Robust Beamforming for HAP Downlink Communications
A vision-language LLM forecasts HAP attitudes from telemetry for proactive beamforming, achieving 22.1% higher user service ratio and 12.5% higher sum-rate than baselines in simulations with mean latency of 36 ms.
-
MM-Telco: Benchmarks and Multimodal Large Language Models for Telecom Applications
MM-Telco creates multimodal benchmarks for telecom and demonstrates that fine-tuned LLMs and VLMs achieve significant performance gains on domain-specific tasks.
-
Against the Monolithic Wireless World Model: Why NextG Needs Composable and Agentic Intelligence
Argues that wireless data's configuration dependence and lack of self-containment make monolithic foundation models unsuitable for AI-native 6G, favoring instead composable agentic architectures.
-
Customized Generative AI Agent for Transportation Engineering Practice: A Development and Continued Pre-training Guideline
LoRA continued pretraining on a small U.S. transportation corpus lifts BLEU-4 and ROUGE for Qwen2.5-7B and LLaMA-3.1-8B far above the other four models tested.