Guest Editors
Prof. Aytuğ ONAN
Email: aytug.onan@ikcu.edu.tr
Affiliation: Department of Computer Engineering, İzmir Institute of Technology (IZTECH), İzmir, 35430, Türkiye
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Research Interests: Natural Language Processing, Ensemble Learning, Large Language Models
Summary
Contrastive learning has emerged as a powerful paradigm for enhancing the representational quality, robustness, and alignment of large language models (LLMs). By learning to distinguish semantically similar and dissimilar examples, contrastive methods have shown remarkable success across a wide range of NLP tasks, including sentence embedding, information retrieval, summarization, and dialogue modeling. This special issue invites high-quality contributions exploring the foundations, innovations, and applications of contrastive learning within the context of large language models.
We welcome theoretical studies, methodological advancements, and real-world applications that utilize contrastive objectives during pretraining, fine-tuning, or task-specific adaptation. Topics of interest include (but are not limited to) supervised and self-supervised contrastive learning, multi-view representation learning, contrastive alignment for multi-modal LLMs, domain adaptation, data efficiency, interpretability, and adversarial robustness. Studies that benchmark contrastive approaches on diverse NLP datasets, propose novel loss functions, or integrate contrastive learning with reinforcement or instruction tuning are especially encouraged.
This special issue aims to bring together researchers and practitioners working at the intersection of contrastive learning and LLMs, fostering new directions toward more effective, explainable, and generalizable NLP systems.
Keywords
Contrastive Learning, Large Language Models (LLMs), Self-Supervised Learning, Representation Learning, Sentence Embeddings, Semantic Similarity, Pretraining Objectives, Fine-Tuning Strategies, Adversarial Robustness, Cross-Modal Learning
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