Search Results for author: Bailan He

Found 8 papers, 2 papers with code

Red Teaming GPT-4V: Are GPT-4V Safe Against Uni/Multi-Modal Jailbreak Attacks?

no code implementations4 Apr 2024 Shuo Chen, Zhen Han, Bailan He, Zifeng Ding, Wenqian Yu, Philip Torr, Volker Tresp, Jindong Gu

Various jailbreak attacks have been proposed to red-team Large Language Models (LLMs) and revealed the vulnerable safeguards of LLMs.

Understanding and Improving In-Context Learning on Vision-language Models

no code implementations29 Nov 2023 Shuo Chen, Zhen Han, Bailan He, Mark Buckley, Philip Torr, Volker Tresp, Jindong Gu

Our findings indicate that ICL in VLMs is predominantly driven by the textual information in the demonstrations whereas the visual information in the demonstrations barely affects the ICL performance.

In-Context Learning

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

Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information

no code implementations15 Nov 2022 Zifeng Ding, Jingpei Wu, Bailan He, Yunpu Ma, Zhen Han, Volker Tresp

Similar problem exists in temporal knowledge graphs (TKGs), and no previous temporal knowledge graph completion (TKGC) method is developed for modeling newly-emerged entities.

Link Prediction Meta-Learning +1

Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction

no code implementations21 May 2022 Zifeng Ding, Bailan He, Yunpu Ma, Zhen Han, Volker Tresp

In this paper, we follow the previous work that focuses on few-shot relational learning on static KGs and extend two fundamental TKG reasoning tasks, i. e., interpolated and extrapolated link prediction, to the one-shot setting.

Few-Shot Learning Knowledge Graphs +2

A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion

no code implementations14 Dec 2021 Zifeng Ding, Yunpu Ma, Bailan He, Volker Tresp

Knowledge graphs contain rich knowledge about various entities and the relational information among them, while temporal knowledge graphs (TKGs) describe and model the interactions of the entities over time.

Temporal Knowledge Graph Completion

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