arXiv:2606.08347v1 Announce Type: cross Abstract: Modern language models represent text using discrete token-level embeddings, which forces recurring multi-token patterns to be learned implicitly across Transformer layers. Both Over-tokenized Transformers and Engram attempt to address this limitation by explicitly incorporating multi-token (n-gram) memories. However, they rely on separate hash tables for each n-gram order, which introduces hash collisions and prevents nested n-grams from sharing the underlying latent structures. To address these issues, we propose Tensorized Engram (TN-gram),
This paper addresses current limitations in Large Language Models' (LLMs) ability to efficiently handle multi-token patterns, a critical area for improving their efficiency and performance, particularly as their scale increases.
Improving how LLMs represent and learn multi-token patterns through mechanisms like Tensorized Engram could lead to more efficient, more capable, and potentially smaller models, impacting the cost and accessibility of advanced AI.
The proposed Tensorized Engram offers a method to allow nested n-grams to share underlying latent structures, moving beyond discrete token-level embeddings and potentially reducing hash collisions found in previous approaches.
- · AI model developers
- · Cloud AI providers
- · Researchers in NLP
- · Less efficient LLM architectures
- · Users with high inference costs
More sophisticated and computationally efficient LLMs could emerge, requiring less memory and compute for similar performance.
This could enable the deployment of advanced AI models in resource-constrained environments or facilitate more complex on-device AI applications.
Increased efficiency might lower the barrier to entry for developing and deploying AI, accelerating the spread of sophisticated AI capabilities across various industries.
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Read at arXiv cs.LG