Papers with embedding model development

1 papers
Embedding-Converter: A Unified Framework for Cross-Model Embedding Transformation (2025.acl-long)

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Challenge: Embedding models are fundamental to modern machine learning, but the continuous development of new models presents a major challenge.
Approach: They propose a framework for efficiently transforming embeddings between different models, thus avoiding costly ‘re-embedding’.
Outcome: The proposed framework achieves 100 times faster and cheaper computations in real-world applications.

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