2 papers
cs.CL2024
DiLM: Distilling Dataset into Language Model for Text-level Dataset Distillation
Aru Maekawa, Satoshi Kosugi, Kotaro Funakoshi +1
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 th…
cs.CL2024
Can we obtain significant success in RST discourse parsing by using Large Language Models?
Aru Maekawa, Tsutomu Hirao, Hidetaka Kamigaito +1
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 (N…