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DiLM: Distilling Dataset into Language Model for Text-level Dataset Distillation
To address this issue, we propose a novel text dataset distillation approach, called Distilling dataset into Language Model (DiLM), which trains a language model to generate informative synthetic training samples as text data, instead of directly optimizing synthetic samples.
Can we obtain significant success in RST discourse parsing by using Large Language Models?
Recently, decoder-only pre-trained large language models (LLMs), with several tens of billion parameters, have significantly impacted a wide range of natural language processing (NLP) tasks.
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