5 citations · 11 across the 9 of their papers we have counts for
8 papers · 1 filter
Large Language Models for Wireless Networks: An Overview from the Prompt Engineering Perspective
Hao Zhou, Chengming Hu, Dun Yuan +5
Recently, large language models (LLMs) have been successfully applied to many fields, showing outstanding comprehension and reasoning capabilities. Despite their great potential, L…
ParetoFlow: Guided Flows in Multi-Objective Optimization
Ye Yuan, Can Chen, Christopher Pal +1
In offline multi-objective optimization (MOO), we leverage an offline dataset of designs and their associated labels to simultaneously minimize multiple objectives. This setting mo…
Generative AI as a Service in 6G Edge-Cloud: Generation Task Offloading by In-context Learning
Hao Zhou, Chengming Hu, Dun Yuan +5
Generative artificial intelligence (GAI) is a promising technique towards 6G networks, and generative foundation models such as large language models (LLMs) have attracted consider…
Large Language Model (LLM)-enabled In-context Learning for Wireless Network Optimization: A Case Study of Power Control
Hao Zhou, Chengming Hu, Dun Yuan +4
Large language model (LLM) has recently been considered a promising technique for many fields. This work explores LLM-based wireless network optimization via in-context learning. T…
Retrieval-Augmented Generation for Natural Language Processing: A Survey
Shangyu Wu, Ying Xiong, Yufei Cui +8
Large language models (LLMs) have achieved strong empirical performance in various fields, benefiting from their huge amount of parameters that store knowledge. However, LLMs still…
Design Editing for Offline Model-based Optimization
Ye Yuan, Youyuan Zhang, Can Chen +5
Offline model-based optimization (MBO) aims to maximize a black-box objective function using only an offline dataset of designs and scores. These tasks span various domains, such a…
Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities
Hao Zhou, Chengming Hu, Ye Yuan +11
Large language models (LLMs) have received considerable attention recently due to their outstanding comprehension and reasoning capabilities, leading to great progress in many fiel…
Learning to Extract Structured Entities Using Language Models
Haolun Wu, Ye Yuan, Liana Mikaelyan +4
Recent advances in machine learning have significantly impacted the field of information extraction, with Language Models (LMs) playing a pivotal role in extracting structured info…