output
20162026
most citedChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models

70 citations

Showing 2023Show all

6 papers · 1 filter

cs.CR2023★ 3 cited

LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion Detection

Aiheng Zhang, Qiguang Jiang, Kai Wang +1

With the development of intelligent transportation systems, vehicles are exposed to a complex network environment. As the main network of in-vehicle networks, the controller area n…

cs.CL2023★ 70 cited

ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models

Haoran Luo, Haihong E, Zichen Tang +9

Knowledge Base Question Answering (KBQA) aims to answer natural language questions over large-scale knowledge bases (KBs), which can be summarized into two crucial steps: knowledge…

cs.AI2023★ 18 cited

HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Level

Haoran Luo, Haihong E, Yuhao Yang +7

Link Prediction on Hyper-relational Knowledge Graphs (HKG) is a worthwhile endeavor. HKG consists of hyper-relational facts (H-Facts), composed of a main triple and several auxilia…

quant-ph2023★ 3 cited

Logarithmic light cone, slow entanglement growth, and quantum memory

Yu Zeng, Alioscia Hamma, Yu-Ran Zhang +4

Effective light cones, characterized by Lieb-Robinson bounds, emerge in nonrelativistic local quantum systems. Here, we present several analytical results derived from logarithmic…

quant-ph2023★ 10 cited

Control the qubit-qubit coupling in the superconducting circuit with double-resonator couplers

Hui Wang, Yan-Jun Zhao, Hui-Chen Sun +5

We propose a scheme of using two fixed frequency resonator couplers to tune the coupling strength between two Xmon qubits. The induced indirect qubit-qubit interactions by two reso…

cs.CV2023★ 12 cited

Rethinking Precision of Pseudo Label: Test-Time Adaptation via Complementary Learning

Jiayi Han, Longbin Zeng, Liang Du +2

In this work, we propose a novel complementary learning approach to enhance test-time adaptation (TTA), which has been proven to exhibit good performance on testing data with distr…