Abstract
Low-Rank Adaptation (LoRA) is a widely used Parameter-Efficient Fine-Tuning (PEFT) method that updates an initial weight matrix W0 with a delta matrix 𝛥 W consisted by two low-rank matrices A and B. A previous study suggested that there is correlation between W0 and 𝛥 W. In this study, we aim to delve deeper into relationships between W0 and low-rank matrices A and B to further comprehend the behavior of LoRA. In particular, we analyze a conversion matrix that transform W0 into low-rank matrices, which encapsulates information about the relationships. Our analysis reveals that the conversion matrices are similar across each layer. Inspired by these findings, we hypothesize that a single linear layer, which takes each layer’s W0 as input, can yield task-adapted low-rank matrices. To confirm this hypothesis, we devise a method named Conditionally Parameterized LoRA (CondLoRA) that updates initial weight matrices with low-rank matrices derived from a single linear layer. Our empirical results show that CondLoRA maintains a performance on par with LoRA, despite the fact that the trainable parameters of CondLoRA are fewer than those of LoRA. Therefore, we conclude that “a single linear layer yields task-adapted low-rank matrices.” The code used in our experiments is available at https://github.com/CyberAgentAILab/CondLoRA.- Anthology ID:
- 2024.lrec-main.141
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 1602–1608
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.141
- DOI:
- Cite (ACL):
- Hwichan Kim, Shota Sasaki, Sho Hoshino, and Ukyo Honda. 2024. A Single Linear Layer Yields Task-Adapted Low-Rank Matrices. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 1602–1608, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- A Single Linear Layer Yields Task-Adapted Low-Rank Matrices (Kim et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.141.pdf