Postdoctoral Researcher at the intersection of Topological Deep Learning and Representation Learning — applying Topological Data Analysis (TDA) to Natural Language Processing and Task-Oriented Dialogue Systems.
- 🏛️ Member of the Dialog Systems & Machine Learning Lab (Prof. Milica Gašić), Heinrich-Heine-Universität Düsseldorf
- 🎓 Previously: PhD in Low-Dimensional Topology (Max Planck Institute for Mathematics Bonn & University of Bonn)
- 🙂 Pronouns: he/him
Less is More: Local Intrinsic Dimensions of Contextual Language Models
Benjamin Ruppik et al., NeurIPS 2025
We analyze the geometry of contextual embedding spaces via local intrinsic dimension (LID) to track phenomena like overfitting and grokking; decreasing mean LID aligns with performance gains.
NeurIPS page · arXiv:2506.01034 · 📦 Code: Topo_LLM_public · grokking-via-lid
Local Topology Measures of Contextual Language Model Latent Spaces With Applications to Dialogue Term Extraction
Benjamin Ruppik et al., SIGDIAL 2024 — Best Paper Nomination
Contextual topological features derived from a corpus improve a tagging task on dialogue data.
ACL Anthology · doi:10.18653/v1/2024.sigdial-1.31 · arXiv:2408.03706 · 📦 Code: tda4contextualembeddings-public (GitLab)
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