Kronecker Embeddings: Byte-Level Structured Token Representations for Parameter-Efficient Language Models
arXiv:2605.29459v1 Announce Type: cross Abstract: Large language models route every input through a learned embedding table of shape |V| x d_model, consuming hundreds of millions to billions of trainable parameters at frontier scale. We introduce Kronecker Embeddings, a deterministic byte-level character-position factorization that replaces this table with a fixed encoder and a single learned projection, compatible with standard BPE tokenizers, eliminating 91--94% of input-side trainable parameters at frontier scale. We provide five contributions. First, a cross-model probe across six LMs (135
The paper addresses the significant challenge of increasing parameter counts in large language models, driven by the ongoing pursuit of more efficient and scalable AI architectures.
Reducing model parameter size, particularly in embedding layers, directly impacts the cost, memory footprint, and deployment scalability of AI, making advanced models more accessible and sustainable.
Language models could become significantly more parameter-efficient, reducing the computational and financial barriers to entry for developing and deploying large AI systems.
- · AI developers with resource constraints
- · On-device AI applications
- · Hardware manufacturers (indirectly, via wider adoption)
- · Cloud AI providers (reduced operational costs)
- · AI models reliant on historically large embedding tables
Reduced memory and computational requirements for training and operating large language models.
This efficiency could accelerate the development of specialized or embedded large language models for diverse applications.
Lowering the resource barrier might lead to a democratization of advanced AI capabilities, fostering innovation outside of well-funded labs.
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Read at arXiv cs.LG