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Chat3GPP: An Open-Source Retrieval-Augmented Generation Framework for 3GPP Documents

Long Huang, Ming Zhao, Limin Xiao, Xiujun Zhang, Jungang Hu

TL;DR

Chat3GPP introduces an open-source retrieval-augmented generation framework tailored to 3GPP documents, addressing the complexity and update frequency of telecom standards without domain-specific fine-tuning. It combines hierarchical chunking, hybrid BM25-embedding pre-ranking, a cross-encoder reranker, and prompt-driven generation to deliver accurate responses from large 3GPP corpora. Evaluations on TeleQnA and Tele-Eval demonstrate superior accuracy and robustness against strong telecom baselines, while Elasticsearch-based on-demand retrieval improves memory efficiency compared to FAISS-based approaches. The framework is designed to be adaptable to other technical standards and supports downstream tasks like protocol generation and code automation, with future work focusing on fine-tuning integration and multi-modal data support.

Abstract

The 3rd Generation Partnership Project (3GPP) documents is key standards in global telecommunications, while posing significant challenges for engineers and researchers in the telecommunications field due to the large volume and complexity of their contents as well as the frequent updates. Large language models (LLMs) have shown promise in natural language processing tasks, but their general-purpose nature limits their effectiveness in specific domains like telecommunications. To address this, we propose Chat3GPP, an open-source retrieval-augmented generation (RAG) framework tailored for 3GPP specifications. By combining chunking strategies, hybrid retrieval and efficient indexing methods, Chat3GPP can efficiently retrieve relevant information and generate accurate responses to user queries without requiring domain-specific fine-tuning, which is both flexible and scalable, offering significant potential for adapting to other technical standards beyond 3GPP. We evaluate Chat3GPP on two telecom-specific datasets and demonstrate its superior performance compared to existing methods, showcasing its potential for downstream tasks like protocol generation and code automation.

Chat3GPP: An Open-Source Retrieval-Augmented Generation Framework for 3GPP Documents

TL;DR

Chat3GPP introduces an open-source retrieval-augmented generation framework tailored to 3GPP documents, addressing the complexity and update frequency of telecom standards without domain-specific fine-tuning. It combines hierarchical chunking, hybrid BM25-embedding pre-ranking, a cross-encoder reranker, and prompt-driven generation to deliver accurate responses from large 3GPP corpora. Evaluations on TeleQnA and Tele-Eval demonstrate superior accuracy and robustness against strong telecom baselines, while Elasticsearch-based on-demand retrieval improves memory efficiency compared to FAISS-based approaches. The framework is designed to be adaptable to other technical standards and supports downstream tasks like protocol generation and code automation, with future work focusing on fine-tuning integration and multi-modal data support.

Abstract

The 3rd Generation Partnership Project (3GPP) documents is key standards in global telecommunications, while posing significant challenges for engineers and researchers in the telecommunications field due to the large volume and complexity of their contents as well as the frequent updates. Large language models (LLMs) have shown promise in natural language processing tasks, but their general-purpose nature limits their effectiveness in specific domains like telecommunications. To address this, we propose Chat3GPP, an open-source retrieval-augmented generation (RAG) framework tailored for 3GPP specifications. By combining chunking strategies, hybrid retrieval and efficient indexing methods, Chat3GPP can efficiently retrieve relevant information and generate accurate responses to user queries without requiring domain-specific fine-tuning, which is both flexible and scalable, offering significant potential for adapting to other technical standards beyond 3GPP. We evaluate Chat3GPP on two telecom-specific datasets and demonstrate its superior performance compared to existing methods, showcasing its potential for downstream tasks like protocol generation and code automation.
Paper Structure (18 sections, 1 figure, 3 tables, 1 algorithm)

This paper contains 18 sections, 1 figure, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. LLMs in Telecommunications
  4. RAG
  5. Methodology
  6. Data Pre-processing
  7. Indexing Phase
  8. Chunking Strategy
  9. Embedding
  10. Indexing strategy
  11. Retrieval Phase
  12. Pre-ranking
  13. Ranking
  14. Generation Phase
  15. Experiments
  16. ...and 3 more sections

Figures (1)

  • Figure 1: The overview of the proposed Chat3GPP.