MM-Telco: Benchmarks and Multimodal Large Language Models for Telecom Applications
This work addresses domain-specific adaptation problems for telecom professionals, but it is incremental as it builds on existing LLMs with specialized benchmarks.
The authors tackled the challenge of adapting large language models (LLMs) to telecommunications by proposing MM-Telco, a suite of multimodal benchmarks and models, which resulted in fine-tuned models showing a significant performance boost on domain-specific tasks.
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.