arXiv:2605.20689v1 Announce Type: cross Abstract: High-dimensional embeddings from large language models impose significant storage and computational costs on vector search systems. Recent embedding compression methods, including Matryoshka-Adaptor (EMNLP 2024), Search-Adaptor (ACL 2024), and SMEC (EMNLP 2025), enable dimensionality reduction through lightweight residual adapters, but their training objectives cause severe overfitting when labeled data is scarce, degrading retrieval performance below the frozen baseline. We propose \textsc{DIVE} (\textbf{D}imensionality reduction with \textbf{
The proliferation of large language models and their high-dimensional embeddings is creating urgent demand for more efficient and cost-effective vector search systems, driving innovation in compression techniques.
This development allows for more efficient deployment and scaling of AI systems by reducing the computational and storage burden of large language model embeddings, making advanced AI more accessible and performant.
New embedding compression methods like DIVE will enable more energy- and compute-efficient AI applications, particularly those reliant on vector databases and retrieval-augmented generation.
- · AI developers
- · Cloud service providers
- · Vector database companies
- · Companies deploying large-scale AI
- · Inefficient embedding storage solutions
- · Systems with high compute/storage costs for embeddings
Reduced operational costs for AI infrastructure that utilizes large language models.
Faster and more scalable AI applications, especially in areas like search, recommendations, and chatbots.
Potential acceleration of AI adoption in industries previously constrained by compute and storage costs.
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Read at arXiv cs.LG