Redundancy, Isotropy, and Intrinsic Dimensionality of Prompt-based Text Embeddings (2025.findings-acl)
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| Challenge: | Prompt-based text embedding models generate task-specific embeddables but have thousands of dimensions . dimensionality reductions for embedded text can result in performance degradations of only the first 25% of the dimensions resulting in a very small degradation . |
| Approach: | They investigate how post-hoc dimensionality reduction affects performance of various tasks . they find that embeddings for classification and clustering exhibit lower intrinsic dimensionalities . |
| Outcome: | The proposed model generates task-specific embeddings upon receiving tailored prompts, but has thousands of dimensions and high storage costs. |
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