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Ruotong Liao
Ruotong Liao
Ludwid-Maximilians-University of Munich
Verified email at dbs.ifi.lmu.de - Homepage
Title
Cited by
Cited by
Year
A systematic survey of prompt engineering on vision-language foundation models
J Gu, Z Han, S Chen, A Beirami, B He, G Zhang, R Liao, Y Qin, V Tresp, ...
arXiv preprint arXiv:2307.12980, 2023
762023
ECOLA: Enhanced temporal knowledge embeddings with contextualized language representations
Z Han*, R Liao*, B Liu, Y Zhang, Z Ding, H Köppl, H Schütze, V Tresp
ACL 2023, Findings of the Association for Computational Linguistics 2023, 2022
13*2022
ForecastTKGQuestions: A Benchmark for Temporal Question Answering and Forecasting over Temporal Knowledge Graphs
Z Ding, Z Li, R Qi, J Wu, B He, Y Ma, Z Meng, S Chen, R Liao, Z Han, ...
International Semantic Web Conference, 541-560, 2023
9*2023
GenTKG: Generative Forecasting on Temporal Knowledge Graph
R Liao, X Jia, Y Ma, V Tresp
NAACL 2024, 2024 Annual Conference of the North American Chapter of the …, 2023
52023
Zero-shot relational learning on temporal knowledge graphs with large language models
Z Ding, H Cai, J Wu, Y Ma, R Liao, B Xiong, V Tresp
NAACL 2024, 2024 Annual Conference of the North American Chapter of the …, 2023
42023
zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models
Z Ding, H Cai, J Wu, Y Ma, R Liao, B Xiong, V Tresp
Proceedings of the 2024 Conference of the North American Chapter of the …, 2024
12024
GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models
R Liao, X Jia, Y Li, Y Ma, V Tresp
Findings of the Association for Computational Linguistics: NAACL 2024, 4303-4317, 2024
12024
GraphextQA: A Benchmark for Evaluating Graph-Enhanced Large Language Models
Y Shen*, R Liao*, Z Han, Y Ma, V Tresp
arXiv preprint arXiv:2310.08487, 2023
12023
EchoScene: Indoor Scene Generation via Information Echo over Scene Graph Diffusion
G Zhai, EP Örnek, DZ Chen, R Liao, Y Di, N Navab, F Tombari, B Busam
ECCV 2024, 2024
2024
Planning and Control using Model-Based Reinforcement Learning
P Czempin, V Friedrich, R Liao, J Nettelnstroth, H Belhassen
2021
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Articles 1–10