2024/06/03 by Rasoul Nikbakht, Nikbakht, Rasoul, Mohamed Benzaghta +3 · 4 citations
Engineering · Social Sciences · #FOS: Computer and information sciences #FOS: Electrical engineering #IPv6, Mobility, Handover, Networks, Security #Information Theory (cs.IT) #Multimedia Communication and Technology #Networking and Internet Architecture (cs.NI) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2406.01768
openalex publication_date 2024/06/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Understanding telecom standards involves sorting through numerous technical documents, such as those produced by the 3rd Generation Partnership Project (3GPP), which is time-consuming and labor-intensive. While large language models (LLMs) can assist with the extensive 3GPP knowledge base, an inclusive dataset is crucial for their effective pre-training and fine-tuning. In this paper, we introduce TSpec-LLM, an open-source comprehensive dataset covering all 3GPP documents from Release 8 to Release 19 (1999--2023). To evaluate its efficacy, we first select a representative sample of 3GPP documents, create corresponding technical questions, and assess the baseline performance of various LLMs. We then incorporate a retrieval-augmented generation (RAG) framework to enhance LLM capabilities by retrieving relevant context from the TSpec-LLM dataset. Our evaluation shows that using a naive-RAG framework on TSpec-LLM improves the accuracy of GPT-3.5, Gemini 1.0 Pro, and GPT-4 from 44%, 46%, and 51% to 71%, 75%, and 72%, respectively.