MM-Telco: Benchmarks and Multimodal Large Language Models for Telecom Applications

TL;DR

This paper introduces MM-Telco, a set of multimodal benchmarks and models designed to adapt large language models for telecom applications, addressing domain-specific challenges and improving performance in real-world tasks.

Contribution

The paper presents a comprehensive telecom-specific multimodal benchmark suite and baseline experiments to enhance LLM adaptation in telecom tasks.

Findings

Fine-tuned models show significant performance improvements.

Benchmark tasks cover network operations, management, and documentation.

Analysis reveals weak areas in current multimodal LLMs.

Abstract

Large Language Models (LLMs) have emerged as powerful tools for automating complex reasoning and decision-making tasks. In telecommunications, they hold the potential to transform network optimization, automate troubleshooting, enhance customer support, and ensure regulatory compliance. However, their deployment in telecom is hindered by domain-specific challenges that demand specialized adaptation. To overcome these challenges and to accelerate the adaptation of LLMs for telecom, we propose MM-Telco, a comprehensive suite of multimodal benchmarks and models tailored for the telecom domain. The benchmark introduces various tasks (both text based and image based) that address various practical real-life use cases such as network operations, network management, improving documentation quality, and retrieval of relevant text and images. Further, we perform baseline experiments with various LLMs and VLMs. The models fine-tuned on our dataset exhibit a significant boost in performance. Our experiments also help analyze the weak areas in the working of current state-of-art multimodal LLMs, thus guiding towards further development and research.