Search Results for author: Han Meng

Found 5 papers, 1 papers with code

Large Language Models in Medical Term Classification and Unexpected Misalignment Between Response and Reasoning

no code implementations19 Dec 2023 Xiaodan Zhang, Sandeep Vemulapalli, Nabasmita Talukdar, Sumyeong Ahn, Jiankun Wang, Han Meng, Sardar Mehtab Bin Murtaza, Aakash Ajay Dave, Dmitry Leshchiner, Dimitri F. Joseph, Martin Witteveen-Lane, Dave Chesla, Jiayu Zhou, Bin Chen

This study assesses the ability of state-of-the-art large language models (LLMs) including GPT-3. 5, GPT-4, Falcon, and LLaMA 2 to identify patients with mild cognitive impairment (MCI) from discharge summaries and examines instances where the models' responses were misaligned with their reasoning.

Decision Making Prompt Engineering

Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT

no code implementations23 Jul 2023 Rui Meng, Fangzhou Zhu, Xiaodong Xu, Liang Jin, Bizhu Wang, Bingxuan Xu, Han Meng, Ping Zhang

Physical-Layer Authentication (PLA) has been recently believed as an endogenous-secure and energy-efficient technique to recognize IoT terminals.

Active Learning

Multi-faceted Graph Attention Network for Radar Target Recognition in Heterogeneous Radar Network

no code implementations10 Jun 2022 Han Meng, Yuexing Peng, Wei Xiang, Xu Pang, Wenbo Wang

In this paper, a two-stream semantic feature fusion model, termed Multi-faceted Graph Attention Network (MF-GAT), is proposed to greatly improve the accuracy in the low SNR region of the heterogeneous radar network.

Graph Attention

Spatio-Temporal-Frequency Graph Attention Convolutional Network for Aircraft Recognition Based on Heterogeneous Radar Network

no code implementations15 Apr 2022 Han Meng, Yuexing Peng, Wenbo Wang, Peng Cheng, Yonghui Li, Wei Xiang

This paper proposes a knowledge-and-data-driven graph neural network-based collaboration learning model for reliable aircraft recognition in a heterogeneous radar network.

Graph Attention

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