TN-gram replaces per-order hash tables in n-gram memory modules with a CP tensor factorization that shares token-position factors and uses order-absorption vectors, achieving comparable or better performance with fewer parameters.
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Tensorizing Engram: Sharing Latents Across N-Gram Embeddings is Beneficial in LLMs
TN-gram replaces per-order hash tables in n-gram memory modules with a CP tensor factorization that shares token-position factors and uses order-absorption vectors, achieving comparable or better performance with fewer parameters.