Tensorized Engram: New Method for Efficient N-Gram Embeddings in Language Models
Researchers have proposed Tensorized Engram (TN-gram), a compact memory module that uses Canonical Polyadic (CP) tensor decomposition to share latent factors across n-gram embeddings in large language models. The work addresses limitations of prior approaches—Over-tokenized Transformers and Engram—which used separate hash tables per n-gram order, causing hash collisions and preventing nested n-grams from sharing structure. TN-gram matches or outperforms existing n-gram modules while using significantly fewer parameters, potentially improving the efficiency of LLM token representation.
A preprint submitted to arXiv on June 6, 2026 introduces Tensorized Engram (TN-gram), a new approach to incorporating multi-token (n-gram) memory into large language models. Standard language models rely on discrete token-level embeddings, requiring recurring multi-token patterns to be learned implicitly through many Transformer layers. Prior methods like Engram and Over-tokenized Transformers explicitly added n-gram memories but stored each n-gram order in separate hash tables, leading to hash collisions and an inability for nested n-grams to share underlying latent representations. TN-gram resolves this by representing n-gram embeddings in Canonical Polyadic (CP) tensor form, learning shared token-position factors alongside order-absorption vectors to encode embeddings across different n-gram orders. Comprehensive experiments reported by the authors show TN-gram matches or exceeds the performance of Engram-style modules while requiring substantially fewer parameters, suggesting a more parameter-efficient path to explicit n-gram memory in LLMs.
What's missing
As a preprint, TN-gram has not yet undergone peer review. The paper's own scope of evaluation—specific benchmarks, model scales tested, and baselines compared—is not detailed in the abstract, leaving open questions about generalizability across diverse architectures and tasks. The degree of parameter reduction achieved and whether gains hold at very large model scales are not specified.
What different sources said
- arXiv cs.LGCenter
Tensorizing Engram: Sharing Latents Across N-Gram Embeddings is Beneficial in LLMs
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