@inproceedings{zhao-etal-2024-length,
title = "Length Extrapolation of Transformers: A Survey from the Perspective of Positional Encoding",
author = "Zhao, Liang and
Feng, Xiachong and
Feng, Xiaocheng and
Zhong, Weihong and
Xu, Dongliang and
Yang, Qing and
Liu, Hongtao and
Qin, Bing and
Liu, Ting",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.582",
doi = "10.18653/v1/2024.findings-emnlp.582",
pages = "9959--9977",
abstract = "Built upon the Transformer, large language models (LLMs) have captured worldwide attention due to their remarkable abilities. Nevertheless, all Transformer-based models including LLMs suffer from a preset length limit and can hardly generalize from short training sequences to longer inference ones, namely, they can not perform **length extrapolation** to handle long sequences. Thus, numerous methods have emerged to enhance the length extrapolation of Transformers. Despite the great research efforts, a systematic survey is still lacking. To fill this gap, we delve into these advances in a unified notation from the perspective of positional encoding (PE), as it has been considered the primary factor on length extrapolation. Specifically, we begin with extrapolatable PEs that have dominated this research field. Then, we dive into extrapolation methods based on them, covering position interpolation and randomized position methods. Finally, several challenges and future directions in this area are highlighted. Through this survey, We aim to enable the reader to gain a deep understanding of existing methods and provide stimuli for future research.",
}
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<abstract>Built upon the Transformer, large language models (LLMs) have captured worldwide attention due to their remarkable abilities. Nevertheless, all Transformer-based models including LLMs suffer from a preset length limit and can hardly generalize from short training sequences to longer inference ones, namely, they can not perform **length extrapolation** to handle long sequences. Thus, numerous methods have emerged to enhance the length extrapolation of Transformers. Despite the great research efforts, a systematic survey is still lacking. To fill this gap, we delve into these advances in a unified notation from the perspective of positional encoding (PE), as it has been considered the primary factor on length extrapolation. Specifically, we begin with extrapolatable PEs that have dominated this research field. Then, we dive into extrapolation methods based on them, covering position interpolation and randomized position methods. Finally, several challenges and future directions in this area are highlighted. Through this survey, We aim to enable the reader to gain a deep understanding of existing methods and provide stimuli for future research.</abstract>
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%0 Conference Proceedings
%T Length Extrapolation of Transformers: A Survey from the Perspective of Positional Encoding
%A Zhao, Liang
%A Feng, Xiachong
%A Feng, Xiaocheng
%A Zhong, Weihong
%A Xu, Dongliang
%A Yang, Qing
%A Liu, Hongtao
%A Qin, Bing
%A Liu, Ting
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F zhao-etal-2024-length
%X Built upon the Transformer, large language models (LLMs) have captured worldwide attention due to their remarkable abilities. Nevertheless, all Transformer-based models including LLMs suffer from a preset length limit and can hardly generalize from short training sequences to longer inference ones, namely, they can not perform **length extrapolation** to handle long sequences. Thus, numerous methods have emerged to enhance the length extrapolation of Transformers. Despite the great research efforts, a systematic survey is still lacking. To fill this gap, we delve into these advances in a unified notation from the perspective of positional encoding (PE), as it has been considered the primary factor on length extrapolation. Specifically, we begin with extrapolatable PEs that have dominated this research field. Then, we dive into extrapolation methods based on them, covering position interpolation and randomized position methods. Finally, several challenges and future directions in this area are highlighted. Through this survey, We aim to enable the reader to gain a deep understanding of existing methods and provide stimuli for future research.
%R 10.18653/v1/2024.findings-emnlp.582
%U https://aclanthology.org/2024.findings-emnlp.582
%U https://doi.org/10.18653/v1/2024.findings-emnlp.582
%P 9959-9977
Markdown (Informal)
[Length Extrapolation of Transformers: A Survey from the Perspective of Positional Encoding](https://aclanthology.org/2024.findings-emnlp.582) (Zhao et al., Findings 2024)
ACL
- Liang Zhao, Xiachong Feng, Xiaocheng Feng, Weihong Zhong, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin, and Ting Liu. 2024. Length Extrapolation of Transformers: A Survey from the Perspective of Positional Encoding. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 9959–9977, Miami, Florida, USA. Association for Computational Linguistics.