MM-Telco: Benchmarks and Multimodal Large Language Models for Telecom Applications
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arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918451163955200 |
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| author | Kumar, Anshul Gupta, Gagan Raj Rai, Manish Chakraborty, Apu Modi, Ashutosh Chaoub, Abdelaali Pramanik, Soumajit Giri, Moyank Holla, Yashwanth Kumar, Sunny Sooraj, M. V. Kiran |
| author_facet | Kumar, Anshul Gupta, Gagan Raj Rai, Manish Chakraborty, Apu Modi, Ashutosh Chaoub, Abdelaali Pramanik, Soumajit Giri, Moyank Holla, Yashwanth Kumar, Sunny Sooraj, M. V. Kiran |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_13131 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MM-Telco: Benchmarks and Multimodal Large Language Models for Telecom Applications Kumar, Anshul Gupta, Gagan Raj Rai, Manish Chakraborty, Apu Modi, Ashutosh Chaoub, Abdelaali Pramanik, Soumajit Giri, Moyank Holla, Yashwanth Kumar, Sunny Sooraj, M. V. Kiran Artificial Intelligence Computer Vision and Pattern Recognition Emerging Technologies Networking and Internet Architecture 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. |
| title | MM-Telco: Benchmarks and Multimodal Large Language Models for Telecom Applications |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition Emerging Technologies Networking and Internet Architecture |
| url | https://arxiv.org/abs/2511.13131 |