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cs.LG2024
MoRe Fine-Tuning with 10x Fewer Parameters
Wenxuan Tan, Nicholas Roberts, Tzu-Heng Huang +5
Parameter-efficient fine-tuning (PEFT) techniques have unlocked the potential to cheaply and easily specialize large pretrained models. However, the most prominent approaches, like…
cs.LG2024
OTTER: Effortless Label Distribution Adaptation of Zero-shot Models
Changho Shin, Jitian Zhao, Sonia Cromp +2
Popular zero-shot models suffer due to artifacts inherited from pretraining. One particularly detrimental issue, caused by unbalanced web-scale pretraining data, is mismatched labe…