2 papers
cs.LG2026
The Appeal and Reality of Recycling LoRAs with Adaptive Merging
Haokun Liu, Gyung Hyun Je, Marco Ciccone +3
The widespread availability of fine-tuned LoRA modules for open pre-trained models has led to an interest in methods that can adaptively merge LoRAs to improve performance. These m…
cs.LG2025
Efficiently Estimating Data Efficiency for Language Model Fine-tuning
Gyung Hyun Je, Colin Raffel
While large language models (LLMs) demonstrate reasonable zero-shot capability across many downstream tasks, fine-tuning is a common practice to improve their performance. However,…