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Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach

15 Oct 2020NAACL 2021 4arXiv:2010.07835archive 2025-07-28

Yue Yu, Simiao Zuo, Haoming Jiang, Wendi Ren, Tuo Zhao, Chao Zhang

Fine-tuned pre-trained language models (LMs) have achieved enormous success in many natural language processing (NLP) tasks, but they still require excessive labeled data in the fine-tuning stage. We study the problem of fine-tuning pre-trained LMs using only weak supervision, without any labeled data. This problem is challenging because the high capacity of LMs makes them prone to overfitting the noisy labels generated by weak supervision. To address this problem, we develop a contrastive self-training framework, COSINE, to enable fine-tuning LMs with weak supervision. Underpinned by contrastive regularization and confidence-based reweighting, this contrastive self-training framework can gradually improve model fitting while effectively suppressing error propagation. Experiments on sequence, token, and sentence pair classification tasks show that our model outperforms the strongest baseline by large margins on 7 benchmarks in 6 tasks, and achieves competitive performance with fully-supervised fine-tuning methods.

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compute_metrics yueyu1030/COSINE/utils.py official repository unverified MIT (permissive) · 643c03f907f7dc7c · report
convert_examples_to_features_re yueyu1030/COSINE/data_loader_new.py official repository unverified MIT (permissive) · d7682df458b2fe00 · report
get_label yueyu1030/COSINE/utils.py official repository unverified MIT (permissive) · 0f7620720c0761a8 · report
tokenize_with_2span yueyu1030/COSINE/data_loader_new.py official repository unverified MIT (permissive) · 71e29a5bf54e04e7 · report
tokenize_with_span yueyu1030/COSINE/data_loader_new.py official repository unverified MIT (permissive) · 7ac17cb99fe26c74 · report

Tasks

Language ModelingLanguage ModellingSentenceSentence-Pair ClassificationSentiment AnalysisText ClassificationWord Sense Disambiguation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Sentiment Analysis IMDb COSINE Accuracy 90.54 #36 of 49 Archive leaderboard report
Text Classification Yelp-2 COSINE Accuracy 95.97% #5 of 5 Archive leaderboard report
Word Sense Disambiguation Words in Context COSINE + Transductive Learning Accuracy 85.3 #1 of 37 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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