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Lngram module learns discrete symbols for improved sequence modeling

Researchers have introduced Lngram, a novel module for sequence modeling that operates in latent space. Unlike previous methods that rely on tokenization, Lngram learns discrete symbols directly from hidden states and performs N-gram lookups. This approach has demonstrated improved performance in long-context language modeling and effectively incorporates domain knowledge when added to pre-trained models. The module also shows promise in vision-language and vision-language-action tasks, suggesting broader applicability beyond text. AI

IMPACT Introduces a new method for sequence modeling that could improve performance and efficiency in various AI tasks.

RANK_REASON The cluster contains a research paper detailing a new module for sequence modeling. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Yunao Zheng, Guoyang Xia, Xiaojie Wang, Lei Ren ·

    Lngram: N-gram Conditional Memory in Latent Space

    arXiv:2605.24869v1 Announce Type: new Abstract: Sequence modeling requires both compositional reasoning and local static knowledge retrieval, yet standard Transformers handle both through dense computation. Engram partially decouples retrieval from the backbone, but its token-bas…