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

arXiv: https://arxiv.org/abs/2511.13131 · HTML: https://mlsanity.com/paper/2511.13131/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 31 of its 52 references that are in the map.
- First posted on arXiv 17 Nov 2025.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/)

## Builds on (most cited first, 30 of 31)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [Vision-Language Models for Vision Tasks: A Survey](https://mlsanity.com/paper/2304.00685/) (2023)
- [WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct](https://mlsanity.com/paper/2308.09583/) (2023)
- [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://mlsanity.com/paper/2304.13712/) (2023)
- [GeoChat: Grounded Large Vision-Language Model for Remote Sensing](https://mlsanity.com/paper/2311.15826/) (2023)
- [AutoDroid: LLM-powered Task Automation in Android](https://mlsanity.com/paper/2308.15272/) (2023)
- [Vision Language Models in Autonomous Driving: A Survey and Outlook](https://mlsanity.com/paper/2310.14414/) (2023)
- [Vision-Language Models for Medical Report Generation and Visual Question Answering: A Review](https://mlsanity.com/paper/2403.02469/) (2024)
- [A Critical Evaluation of Evaluations for Long-form Question Answering](https://mlsanity.com/paper/2305.18201/) (2023)
- [TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge](https://mlsanity.com/paper/2310.15051/) (2023)
- [FinGPT: Large Generative Models for a Small Language](https://mlsanity.com/paper/2311.05640/) (2023)
- [Large Language Models for Telecom: Forthcoming Impact on the Industry](https://mlsanity.com/paper/2308.06013/) (2023)
- [WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence](https://mlsanity.com/paper/2405.17053/) (2024)
- [TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models](https://mlsanity.com/paper/2407.09424/) (2024)
- [Understanding Telecom Language Through Large Language Models](https://mlsanity.com/paper/2306.07933/) (2023)
- [A Continued Pretrained LLM Approach for Automatic Medical Note Generation](https://mlsanity.com/paper/2403.09057/) (2024)
- [TSpec-LLM: An Open-source Dataset for LLM Understanding of 3GPP Specifications](https://mlsanity.com/paper/2406.01768/) (2024)
- [Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunications](https://mlsanity.com/paper/2404.15939/) (2024)
- [Tele-LLMs: A Series of Specialized Large Language Models for Telecommunications](https://mlsanity.com/paper/2409.05314/) (2024)
- [Can LLMs Master Math? Investigating Large Language Models on Math Stack Exchange](https://mlsanity.com/paper/2404.00344/) (2024)
- [SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis](https://mlsanity.com/paper/2301.09201/) (2023)
- [Making Network Configuration Human Friendly](https://mlsanity.com/paper/2309.06342/) (2023)
- [Using Large Language Models to Understand Telecom Standards](https://mlsanity.com/paper/2404.02929/) (2024)
- [Large Language Models in Plant Biology](https://mlsanity.com/paper/2401.02789/) (2024)
- [SEAM: A Stochastic Benchmark for Multi-Document Tasks](https://mlsanity.com/paper/2406.16086/) (2024)
- [Unleashing Multi-Hop Reasoning Potential in Large Language Models through Repetition of Misordered Context](https://mlsanity.com/paper/2410.07103/) (2024)

## Abstract (arXiv)

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.