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Songxin Zhang

2 accepted papers

2025

Exploring Learning Complexity for Efficient Downstream Dataset Pruning

ICLR 2025poster

The ever-increasing fine-tuning cost of large-scale pre-trained models gives rise to the importance of dataset pruning, which aims to reduce dataset size while maintaining task performance. However, existing dataset pruning methods require training on the entire dataset, which is impractical for lar…

Cited by 0SourcePDFScholar
2025

Fine-tuning can Help Detect Pretraining Data from Large Language Models

ICLR 2025poster

In the era of large language models (LLMs), detecting pretraining data has been increasingly important due to concerns about fair evaluation and ethical risks. Current methods differentiate members and non-members by designing scoring functions, like Perplexity and Min-k%. However, the diversity and…

Cited by 1SourcePDFScholar