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

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.

Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)

arXiv:2608.15436 [cs.AI]

(or arXiv:2608.15436v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.15436 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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