LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extraction
Er Jin, Qihui Feng, Yongli Mou, Gerhard Lakemeyer, Stefan Decker, Oliver Simons and Johannes Stegmaier, LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extraction. Proceedings of the AAAI Conference on Artificial Intelligence. 39, 4 (Apr. 2025), 4129-4137. DOI:https://doi.org/10.1609/aaai.v39i4.32433.
RadLink: Linking Clinical Entities from Radiology Reports
Yongli Mou, Hanbin Chen, Gwendolyn Isabella Lode, Daniel Truhn, Sulayman Sowe and Stefan Decker, „RadLink: Linking Clinical Entities from Radiology Reports,“ 2024 2nd International Conference on Foundation and Large Language Models (FLLM), Dubai, United Arab Emirates, 2024, pp. 443-449, doi: 10.1109/FLLM63129.2024.10852450.
The Design and Implementation of APLOS: An Automated PoLicy DOcument Summarisation System
Sulayman Sowe, Tobias Kiel, Alexander Neumann, Yongli Mou, Vassilios Peristeras and Stefan Decker, „The Design and Implementation of APLOS: An Automated PoLicy DOcument Summarisation System,“ 2024 2nd International Conference on Foundation and Large Language Models (FLLM), Dubai, United Arab Emirates, 2024, pp. 345-356, doi: 10.1109/FLLM63129.2024.10852442.
Understanding Open Source Large Language Models: An Exploratory Study
Sulayman Sowe, Yongli Mou, Du Cheng, Lingxiao Kong, Alexander Tobias Neumann and Stefan Decker, „Understanding Open Source Large Language Models: An Exploratory Study,“ 2024 2nd International Conference on Foundation and Large Language Models (FLLM), Dubai, United Arab Emirates, 2024, pp. 132-140, doi: 10.1109/FLLM63129.2024.10852438.
Knowledge-Enhanced Domain-Specific Continual Learning and Prompt-Tuning of Large Language Models for Ontology Learning
Yixin Peng, Yongli Mou, Bozhen Zhu, Sulayman Sowe, and Stefan Decker, “RWTH-DBIS at LLMs4OL 2024 Tasks A and B: Knowledge-Enhanced Domain-Specific Continual Learning and Prompt-Tuning of Large Language Models for Ontology Learning”, Open Conf Proc, vol. 4, pp. 49–63, Oct. 2024.
Leveraging LLMs Few-shot Learning to Improve Instruction-driven Knowledge Graph Construction
Yongli Mou, “Leveraging LLMs Few-shot Learning to Improve Instruction-driven Knowledge Graph Construction,” VLDB Workshop, Jan. 2025, [Online]. Available: https://vldb.org/workshops/2024/proceedings/LLM+KG/LLM+KG-8.pdf
Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models
Marianna Nezhurina, Lucia Cipolina-Kun, Mehdi Cherti, and Jenia Jitsev, “Alice in Wonderland: Simple Tasks showing complete reasoning Breakdown in State-Of-the-Art Large Language Models,” arXiv (Cornell University), Jun. 2024, doi: 10.48550/arxiv.2406.02061.
Resolving Discrepancies in Compute-Optimal Scaling of Language Models
Tomer Porian, Mitchell Wortsman, Jenia Jitsev, Ludwig Schmidt, and Yair Carmon, “Resolving discrepancies in Compute-Optimal scaling of language models,” arXiv (Cornell University), Jun. 2024, doi: 10.48550/arxiv.2406.19146.
Florenz: Scaling Laws for Systematic Generalization in Vision-Language Models
Julian Spravil, Sebastian Houben, and Sven Behnke, “Florenz: Scaling Laws for Systematic Generalization in Vision-Language Models,” arXiv.org, Mar. 12, 2025. https://arxiv.org/abs/2503.09443
Project Alexandria: Towards Freeing Scientific Knowledge from Copyright Burdens via LLMs
Christoph Schuhmann, Jenia Jitsev, “Project Alexandria: Towards Freeing Scientific Knowledge from Copyright Burdens via LLMs,” arXiv.org, Feb. 26, 2025. https://arxiv.org/abs/2502.19413
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