TSpec-LLM: An Open-source Dataset for LLM Understanding of 3GPP Specifications
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913379949477888 |
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| author | Nikbakht, Rasoul Benzaghta, Mohamed Geraci, Giovanni |
| author_facet | Nikbakht, Rasoul Benzaghta, Mohamed Geraci, Giovanni |
| contents | 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 \textit{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 \textit{TSpec-LLM} dataset. Our evaluation shows that using a naive-RAG framework on \textit{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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01768 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TSpec-LLM: An Open-source Dataset for LLM Understanding of 3GPP Specifications Nikbakht, Rasoul Benzaghta, Mohamed Geraci, Giovanni Networking and Internet Architecture Information Theory Signal Processing 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 \textit{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 \textit{TSpec-LLM} dataset. Our evaluation shows that using a naive-RAG framework on \textit{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. |
| title | TSpec-LLM: An Open-source Dataset for LLM Understanding of 3GPP Specifications |
| topic | Networking and Internet Architecture Information Theory Signal Processing |
| url | https://arxiv.org/abs/2406.01768 |