mlsanityData to 14 Oct 2026

DeepSpecs: Expert-Level Questions Answering in 5G

arXiv 2511.01305 ↗First posted 3 Nov 2025cs.CL cs.AI cs.NIcited by 0

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Abstract (arXiv)

5G technology enables mobile Internet access for billions of users. Answering expert-level questions about 5G specifications requires navigating thousands of pages of cross-referenced standards that evolve across releases. Existing retrieval-augmented generation (RAG) frameworks, including telecom-specific approaches, rely on semantic similarity and cannot reliably resolve cross-references or reason about specification evolution. We present DeepSpecs, a RAG system enhanced by structural and temporal reasoning via three metadata-rich databases: SpecDB (clause-aligned specification text), ChangeDB (line-level version diffs), and TDocDB (standardization meeting documents). DeepSpecs explicitly resolves cross-references by recursively retrieving referenced clauses through metadata lookup, and traces specification evolution by mining changes and linking them to Change Requests that document design rationale. We curate two 5G QA datasets: 573 expert-annotated real-world questions from practitioner forums and educational resources, and 350 evolution-focused questions derived from approved Change Requests. Across multiple LLM backends, DeepSpecs outperforms base models and state-of-the-art telecom RAG systems; ablations confirm that explicit cross-reference resolution and evolution-aware retrieval substantially improve answer quality, underscoring the value of modeling the structural and temporal properties of 5G standards.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 33 references that are in the map. First posted on arXiv 3 Nov 2025.

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References · 15

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  3. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,453
  4. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,444
  5. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 952
  6. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 923
  7. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 517
  8. 2024Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluatorscited by 149
  9. 2024TSpec-LLM: An Open-source Dataset for LLM Understanding of 3GPP Specificationscited by 27
  10. 2024Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunicationscited by 26
  11. 2024Tele-LLMs: A Series of Specialized Large Language Models for Telecommunicationscited by 25
  12. 2025MORQA: Benchmarking Evaluation Metrics for Medical Open-Ended Question Answeringcited by 8
3 more
  1. 2025Chat3GPP: An Open-Source Retrieval-Augmented Generation Framework for 3GPP Documentscited by 5
  2. 2025Telco-oRAG: Optimizing Retrieval-augmented Generation for Telecom Queries via Hybrid Retrieval and Neural Routingcited by 3
  3. 2024Technical Language Processing for Telecommunications Specificationscited by 1

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  2. 2025How to Discover Knowledge for FutureG: Contextual RAG and LLM Prompting for O-RAN
  3. 2026H2LooP Telecom Model v1: From Telecom Comprehension to Autonomous Issue and PR Resolution
  4. 2024TeleOracle: Fine-Tuned Retrieval-Augmented Generation with Long-Context Support for Network
  5. 2025Think Less, Label Better: Multi-Stage Domain-Grounded Synthetic Data Generation for Fine-Tuning Large Language Models in Telecommunications
  6. 2025Evaluating Open-Source Large Language Models for Technical Telecom Question Answering
  7. 2025KG2QA: Knowledge Graph-enhanced Retrieval-augmented Generation for Communication Standards Question Answering
  8. 2026TelecomGPT-R1: Unified Post-Training for Reasoning Across Heterogeneous Telecom Tasks