MM-Telco: Benchmarks and Multimodal Large Language Models for Telecom Applications
Summary: arXiv:2511.13131v2 Announce Type: replace
Introduction
Large Language Models (LLMs) have gained prominence in recent years, proving to be instrumental in automating complex reasoning and decision-making tasks across various sectors. The telecommunications industry stands to benefit significantly from these advancements as LLMs offer opportunities to revolutionize network optimization, automate troubleshooting processes, enhance customer support, and ensure regulatory compliance. However, the integration of LLMs into telecommunications is not without its challenges, primarily due to the domain-specific requirements that necessitate specialized adaptations.
Introducing MM-Telco
To address these challenges and facilitate the effective adaptation of LLMs in the telecommunications sector, we present MM-Telco—a comprehensive suite of multimodal benchmarks and models specifically designed for telecom applications. This innovative benchmark encompasses a variety of tasks that are both text-based and image-based, directly targeting practical, real-world use cases prevalent in the industry.
Key Features of MM-Telco
MM-Telco is structured to enhance several critical areas within telecommunications:
- Network Operations: Automating the management and monitoring of network performance.
- Network Management: Streamlining processes related to network configuration and maintenance.
- Improving Documentation Quality: Enhancing the clarity and usefulness of technical documentation.
- Retrieval of Relevant Text and Images: Optimizing the accessibility of pertinent information for users and operators.
Benchmark Tasks
The benchmark integrates a variety of tasks designed to simulate real-life scenarios encountered in telecom operations:
- Text-based tasks focused on troubleshooting guides, customer interactions, and regulatory compliance documentation.
- Image-based tasks aimed at network infrastructure analysis, equipment identification, and visual documentation.
Performance Evaluation and Results
In our research, we conducted baseline experiments utilizing various LLMs and Vision Language Models (VLMs) fine-tuned on the MM-Telco dataset. The results indicated a marked improvement in performance metrics compared to pre-existing models. This validation not only underscores the effectiveness of MM-Telco but also highlights the potential for further advancements in the field. Furthermore, our experiments shed light on existing weaknesses in current state-of-the-art multimodal LLMs, providing a roadmap for future research and development initiatives.
Conclusion
As the telecommunications landscape continues to evolve, the integration of advanced AI technologies like LLMs becomes increasingly vital. MM-Telco represents a significant step forward in tailoring these powerful tools for the unique challenges of the telecom industry. By establishing a robust framework for multimodal benchmarks and models, we pave the way for enhanced operational efficiency, improved customer experiences, and greater compliance with regulatory standards.