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Related papers: LLM4WM: Adapting LLM for Wireless Multi-Tasking

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Downlink channel temporal prediction is a critical technology in massive multiple-input multiple-output (MIMO) systems. However, existing methods that rely on fixed-step historical sequences significantly limit the accuracy, practicality,…

Information Theory · Computer Science 2024-09-04 Shilong Fan , Zhenyu Liu , Xinyu Gu , Haozhen Li

Large artificial intelligence models (LAMs) are transforming wireless physical layer technologies through their robust generalization, multitask processing, and multimodal capabilities. This article reviews recent advancements in applying…

Information Theory · Computer Science 2026-01-21 Jiajia Guo , Yiming Cui , Shi Jin , Jun Zhang

This paper presents a novel methodology of fine-tuning for large language models-dynamic LoRA. Building from the standard Low-Rank Adaptation framework, this methodology further adds dynamic adaptation mechanisms to improve efficiency and…

Computation and Language · Computer Science 2025-01-28 Xiaoxuan Liao , Chihang Wang , Shicheng Zhou , Jiacheng Hu , Hongye Zheng , Jia Gao

In modern wireless communication systems, the effective processing of Channel State Information (CSI) is crucial for enhancing communication quality and reliability. However, current methods often handle different tasks in isolation,…

Signal Processing · Electrical Eng. & Systems 2025-02-27 Jun Jiang , Wenjun Yu , Yuan Gao , Shugong Xu

In recent years, Large Language Models (LLMs) have emerged as a prominent area of interest across various research domains, including Process Mining (PM). Current applications in PM have predominantly centered on prompt engineering…

Computation and Language · Computer Science 2025-09-04 Rafael Seidi Oyamada , Jari Peeperkorn , Jochen De Weerdt , Johannes De Smedt

Efficient indoor wireless network (IWN) planning is crucial for providing high-quality 5G in-building services. However, traditional meta-heuristic and artificial intelligence-based planning methods face significant challenges due to the…

Networking and Internet Architecture · Computer Science 2025-07-28 Jinbo Hou , Stefanos Bakirtzis , Kehai Qiu , Sichong Liao , Hui Song , Haonan Hu , Kezhi Wang , Jie Zhang

Wireless networks are increasingly facing challenges due to their expanding scale and complexity. These challenges underscore the need for advanced AI-driven strategies, particularly in the upcoming 6G networks. In this article, we…

Networking and Internet Architecture · Computer Science 2024-09-13 Jingwen Tong , Jiawei Shao , Qiong Wu , Wei Guo , Zijian Li , Zehong Lin , Jun Zhang

Recent advancements in general-purpose or domain-specific multimodal large language models (LLMs) have witnessed remarkable progress for medical decision-making. However, they are designated for specific classification or generative tasks,…

Computer Vision and Pattern Recognition · Computer Science 2024-09-04 Songtao Jiang , Tuo Zheng , Yan Zhang , Yeying Jin , Li Yuan , Zuozhu Liu

Graph-structured data is prevalent in the real world. Recently, due to the powerful emergent capabilities, Large Language Models (LLMs) have shown promising performance in modeling graphs. The key to effectively applying LLMs on graphs is…

Computation and Language · Computer Science 2024-10-16 Haitong Luo , Xuying Meng , Suhang Wang , Tianxiang Zhao , Fali Wang , Hanyun Cao , Yujun Zhang

While machine learning is widely used to optimize wireless networks, training a separate model for each task in communication and localization is becoming increasingly unsustainable due to the significant costs associated with training and…

Signal Processing · Electrical Eng. & Systems 2025-11-20 Mohammad Cheraghinia , Eli De Poorter , Jaron Fontaine , Kwang Soon Kim , Merouane Debbah , Adnan Shahid

Large Language Models (LLMs) have the potential to revolutionize the Sixth Generation (6G) communication networks. However, current mainstream LLMs generally lack the specialized knowledge in telecom domain. In this paper, for the first…

Signal Processing · Electrical Eng. & Systems 2024-07-15 Hang Zou , Qiyang Zhao , Yu Tian , Lina Bariah , Faouzi Bader , Thierry Lestable , Merouane Debbah

Visual Language Models (VLMs) are now increasingly being merged with Large Language Models (LLMs) to enable new capabilities, particularly in terms of improved interactivity and open-ended responsiveness. While these are remarkable…

In recent years, Large Language Models (LLMs) through Transformer structures have dominated many machine learning tasks, especially text processing. However, these models require massive amounts of data for training and induce high resource…

Machine Learning · Computer Science 2025-04-17 Kilian Pfeiffer , Mohamed Aboelenien Ahmed , Ramin Khalili , Jörg Henkel

With the rapid development of Large Language Models (LLMs), Parameter-Efficient Fine-Tuning (PEFT) methods have gained significant attention, which aims to achieve efficient fine-tuning of LLMs with fewer parameters. As a representative…

Machine Learning · Computer Science 2025-05-30 Dacao Zhang , Kun Zhang , Shimao Chu , Le Wu , Xin Li , Si Wei

To manage and optimize constantly evolving wireless networks, existing machine learning (ML)- based studies operate as black-box models, leading to increased computational costs during training and a lack of transparency in decision-making,…

Signal Processing · Electrical Eng. & Systems 2025-06-10 Hao Zhou , Chengming Hu , Dun Yuan , Ye Yuan , Di Wu , Xue Liu , Jianzhong , Zhang

Table reasoning tasks have shown remarkable progress with the development of large language models (LLMs), which involve interpreting and drawing conclusions from tabular data based on natural language (NL) questions. Existing solutions…

Computation and Language · Computer Science 2024-10-11 Yuan Sui , Jiaru Zou , Mengyu Zhou , Xinyi He , Lun Du , Shi Han , Dongmei Zhang

While general-purpose large language models (LLMs) demonstrate proficiency on multiple tasks within the domain of translation, approaches based on open LLMs are competitive only when specializing on a single task. In this paper, we propose…

Low-rank Adaptation (LoRA) has demonstrated remarkable capabilities for task specific fine-tuning. However, in scenarios that involve multiple tasks, training a separate LoRA model for each one results in considerable inefficiency in terms…

Computation and Language · Computer Science 2025-07-08 Yihua Shao , Minxi Yan , Yang Liu , Siyu Chen , Wenjie Chen , Xinwei Long , Ziyang Yan , Lei Li , Chenyu Zhang , Nicu Sebe , Hao Tang , Yan Wang , Hao Zhao , Mengzhu Wang , Jingcai Guo

The integration of large language model (LLM) and data management (DATA) is rapidly redefining both domains. In this survey, we comprehensively review the bidirectional relationships. On the one hand, DATA4LLM, spanning large-scale data…

The integration of Large Language Models (LLMs) into financial analysis has garnered significant attention in the NLP community. This paper presents our solution to IJCAI-2024 FinLLM challenge, investigating the capabilities of LLMs within…

Computational Engineering, Finance, and Science · Computer Science 2024-07-03 Yupeng Cao , Zhiyuan Yao , Zhi Chen , Zhiyang Deng