@inproceedings{feng-etal-2025-pretrained,
title = "Your Pretrained Model Tells the Difficulty Itself: A Self-Adaptive Curriculum Learning Paradigm for Natural Language Understanding",
author = "Feng, Qi and
Liu, Yihong and
Schuetze, Hinrich",
editor = "Zhao, Jin and
Wang, Mingyang and
Liu, Zhu",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-srw.15/",
pages = "222--239",
ISBN = "979-8-89176-254-1",
abstract = "Curriculum learning is a widely adopted training strategy in natural language processing (NLP), where models are exposed to examples organized by increasing difficulty to enhance learning efficiency and performance. However, most existing approaches rely on manually defined difficulty metrics {--} such as text length {--} which may not accurately reflect the model{'}s own perspective. To overcome this limitation, we present a self-adaptive curriculum learning paradigm that prioritizes fine-tuning examples based on difficulty scores predicted by pre-trained language models (PLMs) themselves. Building on these scores, we explore various training strategies that differ in the ordering of examples for the fine-tuning: from easy-to-hard, hard-to-easy, to mixed sampling. We evaluate our method on four natural language understanding (NLU) datasets covering both binary and multi-class classification tasks.Experimental results show that our approach leads to faster convergence and improved performance compared to standard random sampling."
}
Markdown (Informal)
[Your Pretrained Model Tells the Difficulty Itself: A Self-Adaptive Curriculum Learning Paradigm for Natural Language Understanding](https://preview.aclanthology.org/ingestion-acl-25/2025.acl-srw.15/) (Feng et al., ACL 2025)
ACL