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Aru Maekawa

2 accepted papers

2024

DiLM: Distilling Dataset into Language Model for Text-level Dataset Distillation

NAACL 2024findings

Dataset distillation aims to compress a training dataset by creating a small number of informative synthetic samples such that neural networks trained on them perform as well as those trained on the original training dataset. Current text dataset distillation methods create each synthetic sample as…

2023

Dataset Distillation with Attention Labels for Fine-tuning BERT

ACL 2023short

Dataset distillation aims to create a small dataset of informative synthetic samples to rapidly train neural networks that retain the performance of the original dataset. In this paper, we focus on constructing distilled few-shot datasets for natural language processing (NLP) tasks to fine-tune pre-…

Cited by 20SourcePDFScholar