Papers with contrastive representation learning
Virtual Augmentation Supported Contrastive Learning of Sentence Representations (2022.findings-acl)
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| Challenge: | Despite profound successes, contrastive representation learning relies on carefully designed data augmentations using domain-specific knowledge. |
| Approach: | They propose a virtual augmentation supported Contrastive Learning of sentence representations . they approximate the neighborhood of an instance via its K-nearest in-batch neighbors . |
| Outcome: | The proposed model outperforms existing methods on a wide range of downstream tasks. |
Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities (2022.naacl-main)
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| Challenge: | Identifying related entities and events within and across documents is fundamental to natural language understanding. |
| Approach: | They propose an approach to entity and event coreference resolution using contrastive representation learning. |
| Outcome: | The proposed method achieves state-of-the-art results on key metrics on the ECB+ corpus and is competitive on others. |
ConFEDE: Contrastive Feature Decomposition for Multimodal Sentiment Analysis (2023.acl-long)
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| Challenge: | Multimodal sentiment analysis aims to predict the sentiment of video content. |
| Approach: | They propose a framework that performs contrastive representation learning and contrastive feature decomposition to enhance the representation of multimodal information. |
| Outcome: | The proposed framework outperforms baseline methods on CH-SIMS, MOSI and MOSEI datasets on a range of metrics. |
Improving Large Language Model Safety with Contrastive Representation Learning (2025.emnlp-main)
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| 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. |