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✅   Contrastive learning and mixture of experts enables precise vector embeddings in biological databases

science

The advancement of transformer neural networks has significantly enhanced the performance of sentence similarity models. However, these models often struggle with highly discriminative tasks and generate sub-optimal representations of complex documents such as peer-reviewed scientific literature. With the increased reliance on retrieval augmentation and search, representing structurally and thematically-varied research documents as concise and descriptive vectors is crucial. This study improves...

NCBI     Apr 29, 2025