# SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis

arXiv: https://arxiv.org/abs/2301.09201 · HTML: https://mlsanity.com/paper/2301.09201/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 79 references that are in the map.
- First posted on arXiv 22 Jan 2023.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Neural Network Acceptability Judgments](https://mlsanity.com/paper/1805.12471/) (2018)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents](https://mlsanity.com/paper/1611.04230/) (2016)
- [Structural Scaffolds for Citation Intent Classification in Scientific Publications](https://mlsanity.com/paper/1904.01608/) (2019)
- [Sentence Simplification with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.10931/) (2017)
- [Neural CRF Model for Sentence Alignment in Text Simplification](https://mlsanity.com/paper/2005.02324/) (2020)
- [ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks](https://mlsanity.com/paper/1909.01716/) (2019)
- [Leveraging BERT for Extractive Text Summarization on Lectures](https://mlsanity.com/paper/1906.04165/) (2019)
- [Pretrained Language Models for Sequential Sentence Classification](https://mlsanity.com/paper/1909.04054/) (2019)
- [Controllable Text Simplification with Explicit Paraphrasing](https://mlsanity.com/paper/2010.11004/) (2020)
- [A Supervised Approach to Extractive Summarisation of Scientific Papers](https://mlsanity.com/paper/1706.03946/) (2017)
- [Discourse Level Factors for Sentence Deletion in Text Simplification](https://mlsanity.com/paper/1911.10384/) (2019)
- [Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data](https://mlsanity.com/paper/2109.10856/) (2021)
- [COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval](https://mlsanity.com/paper/2010.12800/) (2020)
- [A Formal Analysis of 5G Authentication](https://mlsanity.com/paper/1806.10360/) (2018)
- [YASO: A Targeted Sentiment Analysis Evaluation Dataset for Open-Domain Reviews](https://mlsanity.com/paper/2012.14541/) (2020)

## Built on by (most cited first, 20 of 20)

- [WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence](https://mlsanity.com/paper/2405.17053/) (2024)
- [TSpec-LLM: An Open-source Dataset for LLM Understanding of 3GPP Specifications](https://mlsanity.com/paper/2406.01768/) (2024)
- [I Don't Know: Explicit Modeling of Uncertainty with an [IDK] Token](https://mlsanity.com/paper/2412.06676/) (2024)
- [Linguistic Intelligence in Large Language Models for Telecommunications](https://mlsanity.com/paper/2402.15818/) (2024)
- [Leveraging Fine-Tuned Retrieval-Augmented Generation with Long-Context Support: For 3GPP Standards](https://mlsanity.com/paper/2408.11775/) (2024)
- [Adapting Foundation Models for Information Synthesis of Wireless Communication Specifications](https://mlsanity.com/paper/2308.04033/) (2023)
- [Enterprise Benchmarks for Large Language Model Evaluation](https://mlsanity.com/paper/2410.12857/) (2024)
- [TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving](https://mlsanity.com/paper/2506.10674/) (2025)
- [Mapping the Landscape of Generative AI in Network Monitoring and Management](https://mlsanity.com/paper/2502.08576/) (2025)
- [CellularLint: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications](https://mlsanity.com/paper/2407.13742/) (2024)
- [Telco-DPR: A Hybrid Dataset for Evaluating Retrieval Models of 3GPP Technical Specifications](https://mlsanity.com/paper/2410.19790/) (2024)
- [Enhancing Large Language Models (LLMs) for Telecom using Dynamic Knowledge Graphs and Explainable Retrieval-Augmented Generation](https://mlsanity.com/paper/2602.17529/) (2026)
- [Evaluating the Efficacy of Large Language Models in Identifying Phishing Attempts](https://mlsanity.com/paper/2404.15485/) (2024)
- [TeleOracle: Fine-Tuned Retrieval-Augmented Generation with Long-Context Support for Network](https://mlsanity.com/paper/2411.02617/) (2024)
- [Technical Language Processing for Telecommunications Specifications](https://mlsanity.com/paper/2406.02325/) (2024)
- [Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation](https://mlsanity.com/paper/2503.24245/) (2025)
- [Beyond Benchmarks: LLM Evaluation with an Anthropomorphic and Lifecycle-oriented Roadmap](https://mlsanity.com/paper/2508.18646/) (2025)
- [Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework](https://mlsanity.com/paper/2501.09631/) (2025)
- [MM-Telco: Benchmarks and Multimodal Large Language Models for Telecom Applications](https://mlsanity.com/paper/2511.13131/) (2025)
- [OTel: Building Domain-Specialized Telecom LLM Foundations for Intelligent Networks](https://mlsanity.com/paper/2608.15436/) (2026)

## Abstract (arXiv)

5G is the 5th generation cellular network protocol. It is the state-of-the-art global wireless standard that enables an advanced kind of network designed to connect virtually everyone and everything with increased speed and reduced latency. Therefore, its development, analysis, and security are critical. However, all approaches to the 5G protocol development and security analysis, e.g., property extraction, protocol summarization, and semantic analysis of the protocol specifications and implementations are completely manual. To reduce such manual effort, in this paper, we curate SPEC5G the first-ever public 5G dataset for NLP research. The dataset contains 3,547,586 sentences with 134M words, from 13094 cellular network specifications and 13 online websites. By leveraging large-scale pre-trained language models that have achieved state-of-the-art results on NLP tasks, we use this dataset for security-related text classification and summarization. Security-related text classification can be used to extract relevant security-related properties for protocol testing. On the other hand, summarization can help developers and practitioners understand the high level of the protocol, which is itself a daunting task. Our results show the value of our 5G-centric dataset in 5G protocol analysis automation. We believe that SPEC5G will enable a new research direction into automatic analyses for the 5G cellular network protocol and numerous related downstream tasks. Our data and code are publicly available.