Papers with black-box baseline models
LDIR: Low-Dimensional Dense and Interpretable Text Embeddings with Relative Representations (2025.findings-acl)
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| Challenge: | Existing text embeddings with high dimensions are difficult to trace and interpret. |
| Approach: | They propose low-dimensional and interpretable text embeddings with relative representations that encode semantic meanings in a vector space where similar texts are close together in the representation space. |
| Outcome: | The proposed embeddings outperform existing models on multiple tasks with fewer dimensions and are lowdimensional and dense while maintaining interpretability. |