Papers by Nadav Benedek
PRILoRA: Pruned and Rank-Increasing Low-Rank Adaptation (2024.findings-eacl)
Copied to clipboard
| Challenge: | Several approaches to parameter-efficient fine-tuning have been proposed . low-rank Adaptation (LoRA) does not consider the varying importance of each layer . |
| Approach: | They propose a method that allocates a different rank for each layer and performs pruning throughout the training process. |
| Outcome: | The proposed method is based on eight GLUE benchmarks and is currently the state of the art. |