M. Amin

PRiSM: Partial Ranking via Inter-layer Semantic Measurement for Efficient Fine-tuning of Language Models

PRiSM is proposed, a training-free approach for layer-wise partial fine-tuning that leverages the cosine similarity between pre-trained aggregate token representations across layers to identify inter-layer relationships and achieves competitive performance compared to full fine-tuning.