@article{loralowrankadaptation2022,
  title = {LoRA: Low-Rank Adaptation of Large Language Models},
  author = {Hu, E. J. and Shen, Y. and Wallis, P. and Allen-Zhu, Z. and Li, Y. and Wang, S. and Wang, L. and Chen, W.},
  year = {2022},
  journal = {ICLR 2022},
  url = {https://arxiv.org/abs/2106.09685},
  abstract = {Hu et al. (2021, published ICLR 2022) propose Low-Rank Adaptation, a parameter-efficient fine-tuning method that inserts trainable low-rank matrix pairs into the weight updates of a frozen pretrained model, dramatically reducing the compute and storage cost of task-specific adaptation. Although the original paper targets large language models, LoRA was rapidly adopted for diffusion models and is now the dominant mechanism for character and style fine-tuning in open-source image-generation pipelines. Character LoRAs and style LoRAs are in active use across animation pre-production, making this a foundational text for understanding how generative tools are customised in practice.},
  keywords = {generative-ai, image-generation, character-animation},
  note = {AI \& Animation Education Knowledge Base}
}