OTel: Building Domain-Specialized Telecom LLM Foundations for Intelligent Networks

2026-08-15

AI Summary

This study addresses the suboptimal performance and resource scarcity of frontier AI in telecommunications by introducing OTel, the first unified open-source telecom AI resource. Through full-parameter post-training, instruction tuning, and safety alignment, we provide 30 baseline models spanning retrieval, reranking, and language modeling, alongside a comprehensive evaluation framework. Experimental results demonstrate state-of-the-art performance, achieving an NDCG@10 of 93.5% for embedding retrieval, an MRR@10 of 0.952 for reranking, and 88.2% accuracy for language models, with cumulative downloads exceeding 16 million. By filling a critical gap in the field, this work establishes a reproducible foundation of specialized large models for intelligent networks, effectively fostering community collaboration and accelerating technological advancement in telecom AI.

Abstract

Frontier AI models have advanced rapidly, but they still struggle with telecom-specific tasks. We present Open Telco (OTel), an open telecom AI resource with derived datasets for retrieval, reranking, instruction tuning, and safety/abstention, plus 30 full-parameter post-trained baselines across embedding, reranking, and language models. The community has already engaged substantially with the resource: as of May 3, 2026, the released models have been downloaded over 16 million times, and the project has received 157+ pieces of media coverage worldwide. Building on prior open telecom datasets and benchmarks, OTel provides documented telecom data sources, held-out evaluation partitions, trained embedding models, rerankers, context-grounded LLMs, and safety/abstention data in one unified resource. OTel post-training improves performance across all three model families: embedding retrieval reaches 93.5% NDCG@10, reranking reaches 0.952 MRR@10, and language-model correctness reaches 88.2%. We release OTel as a reproducible starting point and invite the community to expand the data, improve embedding and reranking models, and build stronger context-grounded telecom LLMs.

Problem

• Telecom LLM
• Domain Specialization
• Intelligent Networks
• Open Resource

Innovation

• Telecom LLM
• Domain-Specialized Post-Training
• Open Telecom AI Resource
• Retrieval and Reranking
• Safety and Abstention