arXiv:2606.25674v1 Announce Type: new Abstract: LLM-based text embedders have substantially improved retrieval and semantic representation quality, but their deployment remains costly: large backbone models slow down embedding inference, while high-dimensional full-precision embeddings impose substantial storage and bandwidth overhead on large-scale indexes. In this paper, we present BITEMBED, an extreme low-bit framework for LLM-based text embedding that jointly targets encoding efficiency and vector storage. BITEMBED converts pretrained LLM backbones into BitNet-style embedding encoders with
The continuous growth of LLM usage and the associated computational costs make efficiency improvements in their deployment increasingly critical.
This development addresses key bottlenecks in the scalability and cost-efficiency of large-scale AI applications, particularly for retrieval and semantic representation.
LLM-based text embedders will become significantly more efficient and cost-effective to deploy, reducing storage and bandwidth requirements.
- · AI platform providers
- · Cloud infrastructure companies
- · Enterprises using LLM-based retrieval systems
- · Providers of less efficient embedding solutions
Reduced operational costs for AI applications using text embeddings become possible.
Broader adoption of sophisticated AI retrieval and semantic search functionalities due to improved affordability and performance.
Enhanced accessibility and democratization of advanced AI capabilities, potentially fueling innovation in new application areas previously uneconomical.
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Read at arXiv cs.CL