no code implementations • 4 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.
no code implementations • 29 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.
1 code implementation • 24 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.
no code implementations • 15 May 2023 • Yushan Liu, Bailan He, Marcel Hildebrandt, Maximilian Buchner, Daniela Inzko, Roger Wernert, Emanuel Weigel, Dagmar Beyer, Martin Berbalk, Volker Tresp
Global crises and regulatory developments require increased supply chain transparency and resilience.
no code implementations • 15 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.
1 code implementation • 12 Aug 2022 • Zifeng Ding, Zongyue Li, Ruoxia Qi, Jingpei Wu, Bailan He, Yunpu Ma, Zhao Meng, Shuo Chen, Ruotong Liao, Zhen Han, Volker Tresp
To this end, we propose ForecastTKGQA, a TKGQA model that employs a TKG forecasting module for future inference, to answer all three types of questions.
no code implementations • 21 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.
no code implementations • 14 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.