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        <title>Transformer Working Memory Enables Regular Language Reasoning And Natural Language Length Extrapolation</title>
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        <namePart type="given">Ta-Chung</namePart>
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            <namePart type="given">Kalika</namePart>
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    <abstract>Unlike recurrent models, conventional wisdom has it that Transformers cannot perfectly model regular languages. Inspired by the notion of working memory, we propose a new Transformer variant named RegularGPT. With its novel combination of Weight-Sharing, Adaptive-Depth, and Sliding-Dilated-Attention, RegularGPT constructs working memory along the depth dimension, thereby enabling efficient and successful modeling of regular languages such as PARITY. We further test RegularGPT on the task of natural language length extrapolation and surprisingly find that it rediscovers the local windowed attention effect deemed necessary in prior work for length extrapolation.</abstract>
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%0 Conference Proceedings
%T Transformer Working Memory Enables Regular Language Reasoning And Natural Language Length Extrapolation
%A Chi, Ta-Chung
%A Fan, Ting-Han
%A Rudnicky, Alexander
%A Ramadge, Peter
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F chi-etal-2023-transformer
%X Unlike recurrent models, conventional wisdom has it that Transformers cannot perfectly model regular languages. Inspired by the notion of working memory, we propose a new Transformer variant named RegularGPT. With its novel combination of Weight-Sharing, Adaptive-Depth, and Sliding-Dilated-Attention, RegularGPT constructs working memory along the depth dimension, thereby enabling efficient and successful modeling of regular languages such as PARITY. We further test RegularGPT on the task of natural language length extrapolation and surprisingly find that it rediscovers the local windowed attention effect deemed necessary in prior work for length extrapolation.
%R 10.18653/v1/2023.findings-emnlp.397
%U https://aclanthology.org/2023.findings-emnlp.397/
%U https://doi.org/10.18653/v1/2023.findings-emnlp.397
%P 5972-5984

Markdown (Informal)

[Transformer Working Memory Enables Regular Language Reasoning And Natural Language Length Extrapolation](https://aclanthology.org/2023.findings-emnlp.397/) (Chi et al., Findings 2023)

• Transformer Working Memory Enables Regular Language Reasoning And Natural Language Length Extrapolation (Chi et al., Findings 2023)

ACL

• Ta-Chung Chi, Ting-Han Fan, Alexander Rudnicky, and Peter Ramadge. 2023. Transformer Working Memory Enables Regular Language Reasoning And Natural Language Length Extrapolation. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 5972–5984, Singapore. Association for Computational Linguistics.