CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising

arXiv:2607.28236 · cs.AI, cs.CL · Submitted 2026-07-30 · Read on arXiv

cs.AI, cs.CL

Submitted: 2026-07-30

Updated: 2026-10-16

Comments: Submitted to 16th International Conference on Computer and Knowledge Engineering (ICCKE 2026)

Code: https://github.com/ComputationIASBS/CDAE

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Embeddings — 26× in this paper · explained in 2 episodes
• Robustness — 21× in this paper · explained in 6 episodes
• Representation Learning — 14× in this paper · explained in 3 episodes
• Decoder — 8× in this paper · explained in 2 episodes
• Editor — 8× in this paper · explained in 2 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• Contrastive Learning — 7× in this paper · explained in 9 episodes
• Denoising Autoencoder — 7× in this paper · explained in 2 episodes
• Latent Space — 6× in this paper · explained in 7 episodes
• Semantic Similarity — 5× in this paper · explained in 2 episodes
• Cosine Similarity — 4× in this paper · explained in 3 episodes
• Institude for Advanced Studies in Basic Sciences — 4× in this paper
• Transformers — 4× in this paper · explained in 2 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Downstream Tasks — 3× in this paper · explained in 2 episodes
• Semantic Alignment — 3× in this paper · explained in 6 episodes
• Adam — 2× in this paper · explained in 3 episodes
• Adversarial Robustness — 2× in this paper · explained in 4 episodes
• Core — 2× in this paper · explained in 2 episodes
• InfoNCE Loss — 2× in this paper · explained in 2 episodes
• Transformer Model — 2× in this paper · explained in 2 episodes
• Transformer Models — 2× in this paper · explained in 3 episodes
• Weight Decay — 2× in this paper · explained in 2 episodes
• Augmentation — 1× in this paper · explained in 2 episodes
• BERT Model — 1× in this paper · explained in 2 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Language Models (LLMs) — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 9 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Overfitting — 1× in this paper · explained in 2 episodes
• Papers — 1× in this paper · explained in 3 episodes
• Predictive Coding — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Stanford Natural Language Inference — 1× in this paper
• Training Objective — 1× in this paper · explained in 3 episodes

Sources

• LLMs are Also Effective Embedding Models: An In-depth Overview
• LLM Applications: Current Paradigms and the Next Frontier
• Text Embeddings by Weakly-Supervised Contrastive Pre-training

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