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DeepSpecs: Expert-Level Questions Answering in 5G

Aman Ganapathy Manvattira, Yifei Xu, Ziyue Dang, Songwu Lu

TL;DR

DeepSpecs targets expert-level QA for evolving 5G specifications by incorporating structural and temporal reasoning into a retrieval-augmented framework. It introduces three metadata-rich databases (SpecDB, ChangeDB, TDocDB) to enable clause-level cross-reference resolution and specification-evolution reasoning, and demonstrates these capabilities with two real-world QA datasets. Across multiple backends, DeepSpecs outperforms strong baselines and a state-of-the-art telecom RAG system, with ablations confirming the value of cross-reference and evolution-aware retrieval. The work advances practical telecom QA by providing precise, citation-backed answers and by highlighting the importance of modeling documentation structure and historical changes in standards practice.

Abstract

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.

DeepSpecs: Expert-Level Questions Answering in 5G

TL;DR

DeepSpecs targets expert-level QA for evolving 5G specifications by incorporating structural and temporal reasoning into a retrieval-augmented framework. It introduces three metadata-rich databases (SpecDB, ChangeDB, TDocDB) to enable clause-level cross-reference resolution and specification-evolution reasoning, and demonstrates these capabilities with two real-world QA datasets. Across multiple backends, DeepSpecs outperforms strong baselines and a state-of-the-art telecom RAG system, with ablations confirming the value of cross-reference and evolution-aware retrieval. The work advances practical telecom QA by providing precise, citation-backed answers and by highlighting the importance of modeling documentation structure and historical changes in standards practice.

Abstract

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.

Paper Structure

This paper contains 50 sections, 2 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Contributions.
  3. Related Work
  4. Method
  5. Goal
  6. Overview
  7. Database Construction
  8. Cross-Reference Resolution
  9. Specification-Evolution Reasoning
  10. Answer Generation
  11. Datasets
  12. Real-World QA Dataset
  13. Data Collection.
  14. Human Annotation.
  15. CR-Focused QA Dataset
  16. ...and 35 more sections

Figures (2)

  • Figure 1: Database construction of DeepSpecs. The system builds three vector databases from 5G specs and TDocs, each embedded with rich metadata: SpecDB provides the backbone context; ChangeDB tracks the temporal evolution of specs; TDocDB stores the reasoning behind each change.
  • Figure 2: The retrieval process of DeepSpecs. The system leverages the metadata associated with the chunks in each DB to resolve cross-references and trace changes of specs. This allows DeepSpecs to provide informative context with structural and temporal understanding of 5G specs for question answering.