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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.13954 |
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| _version_ | 1866916581374689280 |
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| author | Huang, Long Zhao, Ming Xiao, Limin Zhang, Xiujun Hu, Jungang |
| author_facet | Huang, Long Zhao, Ming Xiao, Limin Zhang, Xiujun Hu, Jungang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13954 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Chat3GPP: An Open-Source Retrieval-Augmented Generation Framework for 3GPP Documents Huang, Long Zhao, Ming Xiao, Limin Zhang, Xiujun Hu, Jungang Computation and Language Artificial Intelligence Distributed, Parallel, and Cluster Computing Information Retrieval 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. |
| title | Chat3GPP: An Open-Source Retrieval-Augmented Generation Framework for 3GPP Documents |
| topic | Computation and Language Artificial Intelligence Distributed, Parallel, and Cluster Computing Information Retrieval |
| url | https://arxiv.org/abs/2501.13954 |