Domain-adaptation of spherical embeddings. - Mihalis Gongolidis; Jeremy Minton; Ronin Wu; Valentin Stauber; Jason Hoelscher-Obermaier; Viktor Botev (CoRR, 2021)

Leveraging knowledge graphs to update scientific word embeddings using latent semantic imputation. - Jason Hoelscher-Obermaier; Edward Stevinson; Valentin Stauber; Ivaylo Zhelev; Viktor Botev; Ronin Wu; Jeremy Minton (CoRR, 2022)

Detecting Edit Failures In Large Language Models: An Improved Specificity Benchmark. - Jason Hoelscher-Obermaier; Julia Persson; Esben Kran; Ioannis Konstas; Fazl Barez (ACL (Findings), 2023)

Self-Consistency of Large Language Models under Ambiguity. - Henning Bartsch; Ole Jorgensen; Domenic Rosati; Jason Hoelscher-Obermaier; Jacob Pfau (BlackboxNLP@EMNLP, 2023)

Benchmarking and Analyzing In-context Learning, Fine-tuning and Supervised Learning for Biomedical Knowledge Curation: a focused study on chemical entities of biological interest. - Yusuf Abdulle; Emily Groves; Minhong Wang 002; Holger Kunz; Jason Hoelscher-Obermaier; Ronin Wu; Honghan Wu (VLDB Workshops, 2024)

Multi-Agent Security Tax: Trading Off Security and Collaboration Capabilities in Multi-Agent Systems. - Pierre Peigné; Mikolaj Kniejski; Filip Sondej; Matthieu David; Jason Hoelscher-Obermaier; Christian Schröder de Witt; Esben Kran (AAAI, 2025)

Multi-Agent Risks from Advanced AI. - Lewis Hammond; Alan Chan; Jesse Clifton; Jason Hoelscher-Obermaier; Akbir Khan; Euan McLean; Chandler Smith; Wolfram Barfuss; Jakob N. Foerster; Tomas Gavenciak; The Anh Han; Edward Hughes 001; et al. (CoRR, 2025)

Beyond Monoliths: Expert Orchestration for More Capable, Democratic, and Safe Large Language Models. - Philip Quirke; Narmeen Oozeer; Chaithanya Bandi; Amir Abdullah; Jason Hoelscher-Obermaier; Jeff M. Phillips; Joshua Greaves; Clement Neo; Fazl Barez; Shriyash Upadhyay (CoRR, 2025)