Search Results for author: Ruotong Liao

Found 6 papers, 4 papers with code

zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models

1 code implementation15 Nov 2023 Zifeng Ding, Heling Cai, Jingpei Wu, Yunpu Ma, Ruotong Liao, Bo Xiong, Volker Tresp

We first input the text descriptions of KG relations into large language models (LLMs) for generating relation representations, and then introduce them into embedding-based TKGF methods.

Knowledge Graphs Relation +1

GraphextQA: A Benchmark for Evaluating Graph-Enhanced Large Language Models

no code implementations12 Oct 2023 Yuanchun Shen, Ruotong Liao, Zhen Han, Yunpu Ma, Volker Tresp

The proposed dataset is designed to evaluate graph-language models' ability to understand graphs and make use of it for answer generation.

Answer Generation Hallucination +3

GenTKG: Generative Forecasting on Temporal Knowledge Graph

1 code implementation11 Oct 2023 Ruotong Liao, Xu Jia, Yunpu Ma, Yangzhe Li, Volker Tresp

Extensive experiments have shown that GenTKG outperforms conventional methods of temporal relational forecasting with low computation resources using extremely limited training data as few as 16 samples.

Retrieval

A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models

1 code implementation24 Jul 2023 Jindong Gu, Zhen Han, Shuo Chen, Ahmad Beirami, Bailan He, Gengyuan Zhang, Ruotong Liao, Yao Qin, Volker Tresp, Philip Torr

This paper aims to provide a comprehensive survey of cutting-edge research in prompt engineering on three types of vision-language models: multimodal-to-text generation models (e. g. Flamingo), image-text matching models (e. g.

Image-text matching Language Modelling +4

ECOLA: Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations

no code implementations17 Mar 2022 Zhen Han, Ruotong Liao, Jindong Gu, Yao Zhang, Zifeng Ding, Yujia Gu, Heinz Köppl, Hinrich Schütze, Volker Tresp

Since conventional knowledge embedding models cannot take full advantage of the abundant textual information, there have been extensive research efforts in enhancing knowledge embedding using texts.

Knowledge Graph Embedding Link Prediction +1

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