Papers with Supervised contrastive learning
Tighter Clusters, Safer Code? Improving Vulnerability Detection with Enhanced Contrastive Loss (2025.naacl-srw)
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| Challenge: | a recent study shows that supervised contrastive learning (SCL) improves embedding separation but struggles with intra-class clustering . code vulnerability detection is a cornerstone of software security, especially as the world undergoes rapid digitization. |
| Approach: | They propose an extension of SCL with a distance-based regularization term that tightens intra-class clustering while maintaining inter-class separation. |
| Outcome: | The proposed method improves F1 score on CodeBERT and GraphCodeBERT with BCE, BCE + SCL, and BCE+ CESCL. |
Learning Label Hierarchy with Supervised Contrastive Learning (2024.findings-eacl)
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| Challenge: | Existing approaches to supervised contrastive learning treat each class as independent and therefore consider all classes to be equally important. |
| Approach: | They propose a family of Label-Aware SCL methods that incorporate hierarchical information to SCL by leveraging similarities between classes. |
| Outcome: | The proposed method outperforms baseline supervised approaches on three datasets. |
Contextual Interaction for Argument Post Quality Assessment (2023.emnlp-main)
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| Challenge: | Existing methods for assessing the quality of natural language arguments are limited . existing methods focus on evaluating individual argument posts, but they often fail to distinguish between arguments with a narrow quality gap. |
| Approach: | They propose to use supervised contrastive learning to model arguments' quality . large language models with in-context examples harness the power of LLMs . |
| Outcome: | The proposed approach outperforms state-of-the-art models on a publicly available dataset . it shows that the LLMs with in-context examples are more effective than baseline models . |
Revisiting Supervised Contrastive Learning for Microblog Classification (2024.emnlp-main)
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| Challenge: | Existing models for microblog classification use pre-training language models (LMs) however, pre-trained LMs are resource-intensive and not suitable for small labs. |
| Approach: | They propose to fine-tune transformer-based language models with a SCL loss for English microblog classification by comparing two benchmarks. |
| Outcome: | The proposed method has a performance gain of up to 11.9 percentage points across all subtasks. |