arXiv:2606.28057v1 Announce Type: cross Abstract: Language models (LMs) represent tokens using embedding matrices that scale linearly with the vocabulary size. To constrain the parameter footprint, prior work proposes hashing many tokens into a single vector within encoder-only models. While this offers parameter efficiency, many-to-one collisions prevent its use in causal LMs. In this paper, we propose MultiHashFormer, a new framework that allows hash-based autoregression. Each token is represented as a unique hash signature, a short sequence of discrete hash IDs, generated by multiple indepe
The paper addresses a long-standing challenge in balancing language model parameter efficiency with performance by proposing a novel hash-based approach that enables autoregressive capabilities.
This research could significantly reduce the computational and memory footprint of large language models, making them more accessible and deployable in resource-constrained environments.
Traditional token embedding methods may be supplemented or replaced by more parameter-efficient hashing techniques, particularly for generative models.
- · AI hardware manufacturers
- · On-device AI applications
- · Generative AI startups
- · Companies reliant solely on massive, unoptimized LMs
- · Legacy embedding techniques
More efficient large language models become available for a wider range of applications and devices.
Reduced infrastructure costs for deploying and running generative AI could accelerate adoption in new sectors.
Increased accessibility of advanced AI models might democratize AI development, fostering innovation beyond well-funded hyperscalers.
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Read at arXiv cs.LG