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

The growing scale of pre-trained language models poses a challenge in fine-tuning for downstream tasks, especially in resource-constrained settings. Recent studies highlight that not all layers in transformer-based language models contribute equally to downstream task performance, giving rise to various partial fine-tuning strategies. However, current methods often introduce significant training overhead or rely on simple heuristics that yield suboptimal performance and poor generalization. We propose PRiSM (Partial Ranking via inter-layer Semantic Measurement), 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. PRiSM comprises two stages: (i) scoring layers based on their relevance to the task via a single forward pass, and (ii) fine-tuning a subset of block-wise highest-scoring layers, while keeping others frozen. We conduct experiments on 15 diverse NLP datasets, including single-sentence and sentence-pair classification tasks. Our method achieves competitive performance compared to full fine-tuning, with an average training speedup of 1.5× and a reduction of trainable parameters by 75%, and outperforms all the comparative baselines. Additionally, our approach does not cause any notable drop in performance when the domain is changed for the evaluation tasks, demonstrating robust cross-domain generalizability.