10 citations · 10 across the 3 of their papers we have counts for
4 papers · 1 filter
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…
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…
Word Segmentation and Morphological Parsing for Sanskrit
Jingwen Li, Leander Girrbach
We describe our participation in the Word Segmentation and Morphological Parsing (WSMP) for Sanskrit hackathon. We approach the word segmentation task as a sequence labelling task…