1 citations · 2 across the 12 of their papers we have counts for
13 papers
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1
With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…
From USD Scenes to Knowledge Graphs: Zero-Shot Ontology Grounding with LLMs
Jiangtao Shuai, Zongxiong Chen, Manfred Hauswirth +1
Constructing knowledge graphs from 3D simulation scenes is essential for robot task reasoning, but the key bottleneck, grounding scene objects to formal ontology classes, still rel…
NERdME: a Named Entity Recognition Dataset for Indexing Research Artifacts in Code Repositories
Genet Asefa Gesese, Zongxiong Chen, Shufan Jiang +4
Existing scholarly information extraction (SIE) datasets focus on scientific papers and overlook implementation-level details in code repositories. README files describe datasets,…
NFDI4DS Shared Tasks for Scholarly Document Processing
Raia Abu Ahmad, Rana Abdulla, Tilahun Abedissa Taffa +18
Shared tasks are powerful tools for advancing research through community-based standardised evaluation. As such, they play a key role in promoting findable, accessible, interoperab…
HD-NDEs: Neural Differential Equations for Hallucination Detection in LLMs
Qing Li, Jiahui Geng, Zongxiong Chen +5
In recent years, large language models (LLMs) have made remarkable advancements, yet hallucination, where models produce inaccurate or non-factual statements, remains a significant…
VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration
Jiahui Geng, Qing Li, Zongxiong Chen +7
The rapid advancement of vision-language models (VLMs) has brought a lot of attention to their safety alignment. However, existing methods have primarily focused on model undersafe…
A Comprehensive Survey of Machine Unlearning Techniques for Large Language Models
Jiahui Geng, Qing Li, Herbert Woisetschlaeger +6
This study investigates the machine unlearning techniques within the context of large language models (LLMs), referred to as \textit{LLM unlearning}. LLM unlearning offers a princi…
Internal Activation Revision: Safeguarding Vision Language Models Without Parameter Update
Qing Li, Jiahui Geng, Zongxiong Chen +3
Vision-language models (VLMs) demonstrate strong multimodal capabilities but have been found to be more susceptible to generating harmful content compared to their backbone large l…
NFDI4DSO: Towards a BFO Compliant Ontology for Data Science
Genet Asefa Gesese, Jörg Waitelonis, Zongxiong Chen +2
The NFDI4DataScience (NFDI4DS) project aims to enhance the accessibility and interoperability of research data within Data Science (DS) and Artificial Intelligence (AI) by connecti…
Converter: Enhancing Interoperability in Research Data Management
Sefika Efeoglu, Zongxiong Chen, Sonja Schimmler +1
Research Data Management (RDM) is essential in handling and organizing data in the research field. The Berlin Open Science Platform (BOP) serves as a case study that exemplifies th…
PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition Dynamics
Derui Zhu, Dingfan Chen, Qing Li +4
Despite tremendous advancements in large language models (LLMs) over recent years, a notably urgent challenge for their practical deployment is the phenomenon of hallucination, whe…
A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness
Zongxiong Chen, Jiahui Geng, Derui Zhu +5
The aim of dataset distillation is to encode the rich features of an original dataset into a tiny dataset. It is a promising approach to accelerate neural network training and rela…
A Survey on Dataset Distillation: Approaches, Applications and Future Directions
Jiahui Geng, Zongxiong Chen, Yuandou Wang +5
Dataset distillation is attracting more attention in machine learning as training sets continue to grow and the cost of training state-of-the-art models becomes increasingly high.…