73 citations · 186 across the 72 of their papers we have counts for
5 papers · 2 filters
A Systematic Study of In-the-Wild Model Merging for Large Language Models
Oğuz Kağan Hitit, Leander Girrbach, Zeynep Akata
Model merging combines multiple fine-tuned checkpoints into a single model without additional training, offering an attractive approach to reusing models and efficiently improving…
Reference-Free Rating of LLM Responses via Latent Information
Leander Girrbach, Chi-Ping Su, Tankred Saanum +3
How reliable are single-response LLM-as-a-judge ratings without references, and can we obtain fine-grained, deterministic scores in this setting? We study the common practice of as…
Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study
Yiran Huang, Lukas Thede, Massimiliano Mancini +2
While Multimodal Large Language Models (MLLMs) demonstrate impressive capabilities, their substantial computational and memory requirements pose significant barriers to practical d…
Align-then-Unlearn: Embedding Alignment for LLM Unlearning
Philipp Spohn, Leander Girrbach, Jessica Bader +1
As large language models (LLMs) are trained on massive datasets, they have raised significant privacy and ethical concerns due to their potential to inadvertently retain sensitive…
WikiBigEdit: Understanding the Limits of Lifelong Knowledge Editing in LLMs
Lukas Thede, Karsten Roth, Matthias Bethge +2
Keeping large language models factually up-to-date is crucial for deployment, yet costly retraining remains a challenge. Knowledge editing offers a promising alternative, but metho…