Swift Cross-Dataset Pruning: Enhancing Fine-Tuning Efficiency in Natural Language Understanding

• URL: http://arxiv.org/abs/2501.02432v1
• Date: Sun, 05 Jan 2025 03:52:04 GMT
• Title: Swift Cross-Dataset Pruning: Enhancing Fine-Tuning Efficiency in Natural Language Understanding
• Authors: Binh-Nguyen Nguyen, Yang He,
• Abstract summary: Current cross-dataset pruning techniques for fine-tuning often rely on computationally expensive sample ranking processes. We propose Swift Cross-Dataset Pruning (SCDP), which uses TF-IDF embeddings with geometric median to rapidly evaluate sample importance. Experimental results on six diverse datasets demonstrate the effectiveness of our method, spanning various tasks and scales.
• Score: 2.379669478864599
• License:
• Abstract: Dataset pruning aims to select a subset of a dataset for efficient model training. While data efficiency in natural language processing has primarily focused on within-corpus scenarios during model pre-training, efficient dataset pruning for task-specific fine-tuning across diverse datasets remains challenging due to variability in dataset sizes, data distributions, class imbalance and label spaces. Current cross-dataset pruning techniques for fine-tuning often rely on computationally expensive sample ranking processes, typically requiring full dataset training or reference models. We address this gap by proposing Swift Cross-Dataset Pruning (SCDP). Specifically, our approach uses TF-IDF embeddings with geometric median to rapidly evaluate sample importance. We then apply dataset size-adaptive pruning to ensure diversity: for smaller datasets, we retain samples far from the geometric median, while for larger ones, we employ distance-based stratified pruning. Experimental results on six diverse datasets demonstrate the effectiveness of our method, spanning various tasks and scales while significantly reducing computational resources. Source code is available at: https://github.com/he-y/NLP-Dataset-Pruning

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