LLaVE: Large Language and Vision Embedding Models with Hardness-Weighted Contrastive Learning (2025.findings-emnlp)
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| Challenge: | Existing LMM-based embedding models exhibit a high degree of overlap in similarity distribution between positive and negative pairs, making it challenging to distinguish hard negative pairs effectively. |
| Approach: | They propose a framework that improves the embedding model's representation learning for negative pairs based on their discriminative difficulty. |
| Outcome: | The proposed framework improves the embedding model's representation learning for negative pairs based on their discriminative difficulty. |
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