# Telco-oRAG: Optimizing Retrieval-augmented Generation for Telecom Queries via Hybrid Retrieval and Neural Routing

arXiv: https://arxiv.org/abs/2505.11856 · HTML: https://mlsanity.com/paper/2505.11856/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation.
- Builds on 26 of its 36 references that are in the map.
- First posted on arXiv 17 May 2025.

## Placed in

- Retrieval-augmented generation / [RAG robustness and adaptive retrieval](https://mlsanity.com/rag/rag-robustness-and-adaptive-retrieval/)

## Builds on (most cited first, 26 of 26)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://mlsanity.com/paper/2208.03299/) (2022)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [Instruction Tuning for Large Language Models: A Survey](https://mlsanity.com/paper/2308.10792/) (2023)
- [Compacter: Efficient Low-Rank Hypercomplex Adapter Layers](https://mlsanity.com/paper/2106.04647/) (2021)
- [Retrieval-Augmented Generation for AI-Generated Content: A Survey](https://mlsanity.com/paper/2402.19473/) (2024)
- [Matryoshka Representation Learning](https://mlsanity.com/paper/2205.13147/) (2022)
- [How is ChatGPT's behavior changing over time?](https://mlsanity.com/paper/2307.09009/) (2023)
- [The Computational Limits of Deep Learning](https://mlsanity.com/paper/2007.05558/) (2020)
- [Neural Approaches to Conversational AI](https://mlsanity.com/paper/1809.08267/) (2018)
- [Unleashing the potential of prompt engineering for large language models](https://mlsanity.com/paper/2310.14735/) (2023)
- [Domain-specific Knowledge Graphs: A survey](https://mlsanity.com/paper/2011.00235/) (2020)
- [TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge](https://mlsanity.com/paper/2310.15051/) (2023)
- [TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models](https://mlsanity.com/paper/2407.09424/) (2024)
- [Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunications](https://mlsanity.com/paper/2404.15939/) (2024)
- [Tele-LLMs: A Series of Specialized Large Language Models for Telecommunications](https://mlsanity.com/paper/2409.05314/) (2024)
- [TelecomRAG: Taming Telecom Standards with Retrieval Augmented Generation and LLMs](https://mlsanity.com/paper/2406.07053/) (2024)
- [QE-RAG: A Robust Retrieval-Augmented Generation Benchmark for Query Entry Errors](https://mlsanity.com/paper/2504.04062/) (2025)
- [Telecom Language Models: Must They Be Large?](https://mlsanity.com/paper/2403.04666/) (2024)
- [Chat3GPP: An Open-Source Retrieval-Augmented Generation Framework for 3GPP Documents](https://mlsanity.com/paper/2501.13954/) (2025)

## Built on by (most cited first, 3 of 3)

- [TeleTables: A Benchmark for Large Language Models in Telecom Table Interpretation](https://mlsanity.com/paper/2601.04202/) (2025)
- [TelecomGPT-R1: Unified Post-Training for Reasoning Across Heterogeneous Telecom Tasks](https://mlsanity.com/paper/2609.25356/) (2026)
- [DeepSpecs: Expert-Level Questions Answering in 5G](https://mlsanity.com/paper/2511.01305/) (2025)

## Abstract (arXiv)

Artificial intelligence will be one of the key pillars of the next generation of mobile networks (6G), as it is expected to provide novel added-value services and improve network performance. In this context, large language models have the potential to revolutionize the telecom landscape through intent comprehension, intelligent knowledge retrieval, coding proficiency, and cross-domain orchestration capabilities. This paper presents Telco-oRAG, an open-source Retrieval-Augmented Generation (RAG) framework optimized for answering technical questions in the telecommunications domain, with a particular focus on 3GPP standards. Telco-oRAG introduces a hybrid retrieval strategy that combines 3GPP domain-specific retrieval with web search, supported by glossary-enhanced query refinement and a neural router for memory-efficient retrieval. Our results show that Telco-oRAG improves the accuracy in answering 3GPP-related questions by up to 17.6% and achieves a 10.6% improvement in lexicon queries compared to baselines. Furthermore, Telco-oRAG reduces memory usage by 45% through targeted retrieval of relevant 3GPP series compared to baseline RAG, and enables open-source LLMs to reach GPT-4-level accuracy on telecom benchmarks.