Method
CondenseLM
CondenseLM: LLMs-driven Text Dataset Condensation via Reward Matching
Cheng Shen, Yew-Soon Ong, Joey Tianyi Zhou
EMNLP 2025 · first public 2025-01
In one paragraph
Proposes CondenseLM, which uses an LLM-driven pipeline guided by reward matching to generate condensed text-classification datasets directly rather than optimizing embedding-level synthetic samples, aiming to improve representability and coverage over prior text condensation methods; reports it substantially outperforms coreset-selection and dataset-condensation baselines on SST-2, MNLI, AG News and IMDB while reducing computational cost.
Where it sits
- Setting: Text and language models
BibTeX (generated; prefer the venue's official entry)
@article{shen2025condenselm,
title = {CondenseLM: LLMs-driven Text Dataset Condensation via Reward Matching},
author = {Cheng Shen and Yew-Soon Ong and Joey Tianyi Zhou},
journal = {EMNLP 2025},
year = {2025}
}