Abstract: Highlights•The study presents novel methods with similarity metrics to identify false negatives in contrastive learning.•Adaptive false negative elimination and attraction methods are introduced, further boosting performance over SimCSE.•The proposed method demonstrates wide generalizability across various augmentation strategies and existing models.•The method outperforms external SimCSE in detecting false negatives and is more computationally efficient.
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