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

arXiv: https://arxiv.org/abs/2505.14906 · HTML: https://mlsanity.com/paper/2505.14906/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 16 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities](https://mlsanity.com/paper/2405.10825/) (2024)
- [Toward a 6G AI-Native Air Interface](https://mlsanity.com/paper/2012.08285/) (2020)
- [GenIE: Generative Information Extraction](https://mlsanity.com/paper/2112.08340/) (2021)
- [Neural Open Information Extraction](https://mlsanity.com/paper/1805.04270/) (2018)
- [IMoJIE: Iterative Memory-Based Joint Open Information Extraction](https://mlsanity.com/paper/2005.08178/) (2020)
- [Large Language Model (LLM)-enabled In-context Learning for Wireless Network Optimization: A Case Study of Power Control](https://mlsanity.com/paper/2408.00214/) (2024)
- [Self-Refined Generative Foundation Models for Wireless Traffic Prediction](https://mlsanity.com/paper/2408.10390/) (2024)
- [Joint Semantic Communication and Target Sensing for 6G Communication System](https://mlsanity.com/paper/2401.17108/) (2024)

## Built on by (most cited first, 1 of 1)

- [Large Language Models (LLMs) for Telecom Root Cause Analysis (RCA): A Structured Reasoning Framework for Evidence-Grounded Diagnosis](https://mlsanity.com/paper/2609.02805/) (2026)

## 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.