mlsanityData to 7 Oct 2026

Understanding 6G through Language Models: A Case Study on LLM-aided Structured Entity Extraction in Telecom Domain

arXiv 2505.14906 ↗First posted 20 May 2025cs.CL cs.SY eess.SYcited by 1

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Abstract (arXiv)

Knowledge understanding is a foundational part of envisioned 6G networks to advance network intelligence and AI-native network architectures. In this paradigm, information extraction plays a pivotal role in transforming fragmented telecom knowledge into well-structured formats, empowering diverse AI models to better understand network terminologies. This work proposes a novel language model-based information extraction technique, aiming to extract structured entities from the telecom context. The proposed telecom structured entity extraction (TeleSEE) technique applies a token-efficient representation method to predict entity types and attribute keys, aiming to save the number of output tokens and improve prediction accuracy. Meanwhile, TeleSEE involves a hierarchical parallel decoding method, improving the standard encoder-decoder architecture by integrating additional prompting and decoding strategies into entity extraction tasks. In addition, to better evaluate the performance of the proposed technique in the telecom domain, we further designed a dataset named 6GTech, including 2390 sentences and 23747 words from more than 100 6G-related technical publications. Finally, the experiment shows that the proposed TeleSEE method achieves higher accuracy than other baseline techniques, and also presents 5 to 9 times higher sample processing speed.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 16 references that are in the map. First posted on arXiv 20 May 2025.

Builds on

11 of 16 references.

References · 11

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  3. 2018A Survey on Deep Learning for Named Entity Recognitioncited by 281
  4. 2024Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunitiescited by 83
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Large Language Models (LLMs) for Telecom Root Cause Analysis (RCA): A Structured Reasoning Framework for Evidence-Grounded Diagnosiscited by 0

Similar, not cited

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  1. 2025Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation
  2. 2026Enhancing Large Language Models (LLMs) for Telecom using Dynamic Knowledge Graphs and Explainable Retrieval-Augmented Generation
  3. 2026TeleCom-Bench: How Far Are Large Language Models from Industrial Telecommunication Applications?
  4. 2026TeleEmbedBench: A Multi-Corpus Embedding Benchmark for RAG in Telecommunications
  5. 2024TeleOracle: Fine-Tuned Retrieval-Augmented Generation with Long-Context Support for Network
  6. 2026How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks
  7. 2024Telecom Language Models: Must They Be Large?
  8. 2025MM-Telco: Benchmarks and Multimodal Large Language Models for Telecom Applications