Noise Contrastive Estimation-based Matching Framework for Low-Resource Security Attack Pattern Recognition (2024.findings-eacl)
Copied to clipboard
| Challenge: | Techniques, Tactics and Procedures (TTPs) mapping is a difficult task for CTI extraction . conventional learning approaches target the problem in the classical multiclass/label classification setting . |
| Approach: | They propose a neural matching architecture that incorporates a sampling-based learn-to-compare mechanism to facilitate the learning process. |
| Outcome: | The proposed model reduces the complexity of competing over large label space. |
Similar Papers
Improving Large Language Model Safety with Contrastive Representation Learning (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing defenses against large language models (LLMs) are limited by their ability to generate responses to diverse inputs. |
| Approach: | They propose a model defense framework that finetunes a large-scale model using a triplet-based loss combined with adversarial hard negative mining to encourage separation between benign and harmful representations. |
| Outcome: | The proposed model defense outperforms previous representation engineering-based defenses while improving robustness against input-level and embedding-space attacks. |
Learning from Noisy Labels for Entity-Centric Information Extraction (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent information extraction approaches can easily overfit noisy labels and suffer from performance degradation. |
| Approach: | They propose a co-regularization framework for entity-centric information extraction that optimizes neural models with task-specific losses and regularizes them to generate similar predictions based on agreement loss. |
| Outcome: | The proposed framework is optimized with task-specific losses and generates similar predictions based on agreement loss. |
Knowledge Representation Learning with Contrastive Completion Coding (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing knowledge representation learning methods suffer from immaturity on tackling potentially-imperfect knowledge graphs and highly-imbalanced positive-negative instances during training. |
| Approach: | They propose a framework for knowledge representation learning that incorporates two functional components to achieve robust embedding for each entity/relation. |
| Outcome: | The proposed framework achieves better convergence against state-of-the-art methods on several benchmarks. |
Contrastive Data and Learning for Natural Language Processing (2022.naacl-tutorials)
Copied to clipboard
| Challenge: | Current NLP models heavily rely on effective representation learning algorithms. |
| Approach: | This tutorial introduces contrastive learning and provides an introduction to the techniques. |
| Outcome: | This tutorial provides an introduction to the fundamentals of contrastive learning approaches and the theory behind them. |
TLSA: LLM-Guided Text-Label Space Alignment with Contrastive Learning for Generalized Category Discovery (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for generalized category discovery suffer from weak text–label alignment, inconsistent objectives across known and novel categories, and poor discrimination of semantically similar clusters. |
| Approach: | They propose a unified framework that enforces contrastive alignment between text and label representations within a shared semantic space. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on four benchmark datasets. |
Reinforcement Guided Multi-Task Learning Framework for Low-Resource Stereotype Detection (2022.acl-long)
Copied to clipboard
| Challenge: | Existing ‘Stereotype Detection’ datasets adopt a diagnostic approach toward large PLMs. |
| Approach: | They propose a multi-task model that leverages the abundance of data-rich neighboring tasks such as hate speech detection, offensive language detection, misogyny detection, etc., to improve the empirical performance. |
| Outcome: | The proposed model achieves significant gains over baselines on hate speech detection, offensive language detection, misogyny detection, etc. |
Joint Geometrical and Statistical Domain Adaptation for Cross-domain Code Vulnerability Detection (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to detect code vulnerability are limited by labeled training data on target domains. |
| Approach: | They propose a cross-domain code vulnerability detection framework called MNCRI . they propose mutual nearest neighbor contrastive learning to align the source and target domains . |
| Outcome: | The proposed framework outperforms state-of-the-art methods in cross-domain code vulnerability detection tasks. |
CLCL: Non-compositional Expression Detection with Contrastive Learning and Curriculum Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Non-compositional expressions are a substantial challenge for natural language processing systems, necessitating more intricate processing compared to general language tasks. |
| Approach: | They propose a dynamic curriculum learning framework specifically designed to take advantage of scarce available training data for modeling non-compositionality. |
| Outcome: | The proposed framework improves on idiom usage recognition and metaphor detection tasks. |
Difficulty-Focused Contrastive Learning for Knowledge Tracing with a Large Language Model-Based Difficulty Prediction (2024.lrec-main)
Copied to clipboard
Unggi Lee, Sungjun Yoon, Joon Seo Yun, Kyoungsoo Park, YoungHoon Jung, Damji Stratton, Hyeoncheol Kim
| Challenge: | Existing studies have focused on incorporating the difficulty information into knowledge tracing models, but few studies have explored the potential of difficulty estimation. |
| Approach: | They propose a difficulty-centered contrastive learning method and a Large Language Model-based framework for difficulty prediction to improve the performance of knowledge tracing models. |
| Outcome: | The proposed methods demonstrate enhanced performance of knowledge tracing models while ignoring the complex relationship between language and difficulty. |
Applying Contrastive Learning to Code Vulnerability Type Classification (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent approaches to classification of vulnerabilities ignore their relationships and treat each class in isolation, resulting in non-scalable code vector representations. |
| Approach: | They propose a hierarchical contrastive learning framework to bring vector representations of related CWEs closer together and use max-pooling to enable the model to handle longer vulnerability code inputs. |
| Outcome: | The proposed framework outperforms state-of-the-art methods by 2.97%-17.90% on accuracy and 0.98%-22.27% on weighted-F1 with even better performance on higher-quality datasets. |