Summary of Using Large Language Models to Understand Telecom Standards, by Athanasios Karapantelakis and Mukesh Thakur and Alexandros Nikou and Farnaz Moradi and Christian Orlog and Fitsum Gaim and Henrik Holm and Doumitrou Daniil Nimara and Vincent Huang
Using Large Language Models to Understand Telecom Standards
by Athanasios Karapantelakis, Mukesh Thakur, Alexandros Nikou, Farnaz Moradi, Christian Orlog, Fitsum Gaim, Henrik Holm, Doumitrou Daniil Nimara, Vincent Huang
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the use of Large Language Models (LLMs) as Question Answering (QA) assistants for 3GPP document references. It proposes three contributions: a benchmark and measuring methods for evaluating LLM performance, data preprocessing and fine-tuning guidelines to increase response accuracy, and a novel model called TeleRoBERTa that achieves comparable results to foundation models with fewer parameters. The study shows the potential of LLMs as a reliable reference tool for telecom technical documents, applicable in areas such as troubleshooting, maintenance, network operations, and software product development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at using special AI language models called Large Language Models (LLMs) to help find answers to questions about 3GPP documents. The authors share three important findings: a way to test how well LLMs work, tips for making them more accurate, and a new model called TeleRoBERTa that does as well as others but with fewer pieces of information. This could be useful in many areas like fixing problems, maintaining equipment, running networks, and developing software. |
Keywords
» Artificial intelligence » Fine tuning » Question answering
