DeepSpecs: Expert-Level Questions Answering in 5G

2025-11-03

AI Summary

The massive scale, intricate cross-document references, and continuous version evolution of 5G standardization documents render expert-level question answering infeasible for conventional semantic-retrieval-based RAG approaches. Method: We propose a structured and time-aware RAG framework featuring a novel tripartite database architecture—SpecDB (specification knowledge), ChangeDB (change request history), and TDocDB (standardization meeting documents)—enabling recursive cross-specification reference resolution and CR-driven evolutionary path tracing. Our method integrates clause-aligned textual corpora, version-difference analysis, TDoc–specification linkage, and multi-LLM orchestration. Contribution/Results: Experiments demonstrate substantial improvements over baselines and state-of-the-art telecom RAG systems across multiple leading LLMs. Cross-reference resolution and evolution-aware retrieval significantly boost answer accuracy. To our knowledge, this is the first work to systematically incorporate structural and temporal reasoning into standard-document RAG.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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.

Problem

• Resolving cross-references in evolving 5G specifications
• Answering expert-level questions about complex telecom standards
• Tracking specification evolution and design rationale changes

Innovation

• Enhanced RAG system with structural and temporal reasoning
• Explicitly resolves cross-references through metadata lookup
• Traces specification evolution by mining change requests