mlsanityData to 14 Oct 2026

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

arXiv 2511.13131 ↗First posted 17 Nov 2025cs.AI cs.CV cs.ETcited by 0

On the map: Multimodal LLMs / vision-language models → MLLM training and visual instruction tuning

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.

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.

Builds on

31 of 52 references.

References · 31

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19 more
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  1. 2024Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities
  2. 2024MM-LLMs: Recent Advances in MultiModal Large Language Models
  3. 2026TeleCom-Bench: How Far Are Large Language Models from Industrial Telecommunication Applications?
  4. 2024Telecom Language Models: Must They Be Large?
  5. 2026Enhancing Large Language Models (LLMs) for Telecom using Dynamic Knowledge Graphs and Explainable Retrieval-Augmented Generation
  6. 2025TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving
  7. 2025TeleMoM: Consensus-Driven Telecom Intelligence via Mixture of Models ↗
  8. 2026TelcoAgent-Bench: A Multilingual Benchmark for Telecom AI Agents