paper-with-me

Papers

Technical Language Processing for Telecommunications Specifications

2024-06-04 · Felipe A. Rodriguez Y.

Large Language Models (LLMs) are continuously being applied in a more diverse set of contexts. At their current state, however, even state-of-the-art LLMs such as Generative Pre-Trained Transformer 4 (GTP-4) have challenges when extracting information from real-world technical documentation without a heavy preprocessing. One such area with real-world technical documentation is telecommunications engineering, which could greatly benefit from domain-specific LLMs. The unique format and overall structure of telecommunications internal specifications differs greatly from standard English and thus it is evident that the application of out-of-the-box Natural Language Processing (NLP) tools is not a viable option. In this article, we outline the limitations of out-of-the-box NLP tools for processing technical information generated by telecommunications experts, and expand the concept of Technical Language Processing (TLP) to the telecommunication domain. Additionally, we explore the effect of domain-specific LLMs in the work of Specification Engineers, emphasizing the potential benefits of adopting domain-specific LLMs to speed up the training of experts in different telecommunications fields.

📄 PDF Abstract BibTeX arXiv:2406.02325

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

Attention 설명 없음
SET Dynamic Sparse Training method where weight mask is updated randomly periodically
SPEED The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene…
Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…
Layer Normalization Unlike batch normalization, Layer Normalization directly estimates the normalization statistics from the summed inputs…
Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.
BPE Byte Pair Encoding, or BPE, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word…
Label Smoothing Label Smoothing is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the…

Similar Papers 제목 키워드 기반

Chat3GPP: An Open-Source Retrieval-Augmented Generation Framework for 3GPP Documents

2025-01-20 · Long Huang, Ming Zhao, Limin Xiao, Xiujun Zhang 외

The 3rd Generation Partnership Project (3GPP) documents is key standards in global telecommunications, while posing significant challenges for engineers and researchers in the telecommunications field due to the large vo…

ChunkingRAGRetrievalRetrieval-augmented Generation

TelcoAI: Advancing 3GPP Technical Specification Search through Agentic Multi-Modal Retrieval-Augmented Generation

2025-11-17 · Rahul Ghosh, Chun-Hao Liu, Gaurav Rele, Vidya Sagar Ravipati 외 arxiv

The 3rd Generation Partnership Project (3GPP) produces complex technical specifications essential to global telecommunications, yet their hierarchical structure, dense formatting, and multi-modal content make them diffic…

Tele-LLMs: A Series of Specialized Large Language Models for Telecommunications

2024-09-09 · Ali Maatouk, Kenny Chirino Ampudia, Rex Ying, Leandros Tassiulas

The emergence of large language models (LLMs) has significantly impacted various fields, from natural language processing to sectors like medicine and finance. However, despite their rapid proliferation, the applications…

Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunications

2024-04-24 · Andrei-Laurentiu Bornea, Fadhel Ayed, Antonio De Domenico, Nicola Piovesan 외

The application of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems in the telecommunication domain presents unique challenges, primarily due to the complex nature of telecom standard documen…

RAGRetrievalRetrieval-augmented Generation

TeleEmbedBench: A Multi-Corpus Embedding Benchmark for RAG in Telecommunications

2026-04-20 · Pranshav Gajjar, Vijay K Shah arxiv

Large language models (LLMs) are increasingly deployed in the telecommunications domain for critical tasks, relying heavily on Retrieval-Augmented Generation (RAG) to adapt general-purpose models to continuously evolving…