2 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.LG2024
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…