Specializing Large Language Models for Telecom Applications

Large Language Models (LLMs) have become highly proficient in text generation, comprehension, and interaction. Despite their successes across various sectors, their application in the telecommunications industry remains limited. This project focuses on optimizing LLMs for telecom-specific knowledge tasks.

Project Overview

The project utilizes a fine-tuned model called Phi-3-mini-4k-instruct to enhance telecom-specific knowledge tasks. The dataset, derived from the TeleQnA competition, contains telecom-related multiple-choice questions. The goal is to improve model performance through fine-tuning techniques and model-specific optimizations.

Key Features

• Fine-Tuned Model: Specializes the Phi-3-mini-4k-instruct model for telecom applications.
• Telecom-Specific Dataset: Uses data from the TeleQnA competition to train the model.
• Performance Optimization: Applies fine-tuning and model-specific techniques to improve accuracy and relevance.

Skills and Technologies

• Large Language Models (LLM): Core technology for text comprehension and generation.
• Deep Learning: Powers the fine-tuning and optimization processes.
• Machine Learning: Enhances model adaptability to telecom-specific tasks.
• Natural Language Processing (NLP): Drives the understanding and processing of telecom-related text.

This project demonstrates the potential of LLMs in addressing industry-specific challenges, paving the way for their broader adoption in the telecommunications sector.