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        <title>Language Modeling Using Entanglement Enhanced Tensor Trains</title>
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            <namePart type="given">Kai-Wei</namePart>
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    <abstract>Tensor Train Language Models (TTLMs) offer significant memory savings by representing text sequences as tensor networks, but naive implementations struggle with long-range dependencies and limited flexibility. We introduce a modular TTLM framework that combine local and non-local context modules to achieve scalable language modeling. Our non-local modules, inspired by entanglement in quantum information theory, enable efficient modeling of long-range interactions between hidden states. Experiments on Penn Treebank and Wikitext datasets show that our modular TTLM, including entanglement-augmented variants, outperform naive baselines. These results highlight TTLMs as a promising, memory-efficient alternatives for modern language modeling.</abstract>
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%0 Conference Proceedings
%T Language Modeling Using Entanglement Enhanced Tensor Trains
%A Reyes, Ellis
%A Chen, Yi-Shin
%Y Chang, Kai-Wei
%Y Lu, Ke-Han
%Y Yang, Chih-Kai
%Y Tam, Zhi-Rui
%Y Chang, Wen-Yu
%Y Wang, Chung-Che
%S Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)
%D 2025
%8 November
%I Association for Computational Linguistics
%C National Taiwan University, Taipei City, Taiwan
%@ 979-8-89176-379-1
%F reyes-chen-2025-language
%X Tensor Train Language Models (TTLMs) offer significant memory savings by representing text sequences as tensor networks, but naive implementations struggle with long-range dependencies and limited flexibility. We introduce a modular TTLM framework that combine local and non-local context modules to achieve scalable language modeling. Our non-local modules, inspired by entanglement in quantum information theory, enable efficient modeling of long-range interactions between hidden states. Experiments on Penn Treebank and Wikitext datasets show that our modular TTLM, including entanglement-augmented variants, outperform naive baselines. These results highlight TTLMs as a promising, memory-efficient alternatives for modern language modeling.
%U https://aclanthology.org/2025.rocling-main.27/
%P 258-265

Markdown (Informal)

[Language Modeling Using Entanglement Enhanced Tensor Trains](https://aclanthology.org/2025.rocling-main.27/) (Reyes & Chen, ROCLING 2025)

• Language Modeling Using Entanglement Enhanced Tensor Trains (Reyes & Chen, ROCLING 2025)

ACL

• Ellis Reyes and Yi-Shin Chen. 2025. Language Modeling Using Entanglement Enhanced Tensor Trains. In Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025), pages 258–265, National Taiwan University, Taipei City, Taiwan. Association for Computational Linguistics.