3 papers
cs.CL2026
PolyFact: Comparing Consistency-Driven Post-training Methods for Cross-Lingual Factual Recall
Jonathan von Rad, Louis Arts, George Burgess +6
Large language models (LLMs) trained predominantly on English data encode substantial world knowledge, yet often fail to express it reliably in other languages, a phenomenon known…
cs.LG2026
UniComp: A Unified Evaluation of Large Language Model Compression via Pruning, Quantization and Distillation
Jonathan von Rad, Yong Cao, Andreas Geiger
Model compression is increasingly essential for deploying large language models (LLMs), yet existing comparative studies largely focus on pruning and quantization evaluated primari…
cs.LG2026
Investigating the Effect of Network Pruning on Performance and Interpretability
Jonathan von Rad, Florian Seuffert
Deep Neural Networks (DNNs) are often over-parameterized for their tasks and can be compressed quite drastically by removing weights, a process called pruning. We investigate the i…