@inproceedings{xu-etal-2025-enhancing-character,
title = "Enhancing Character-Level Understanding in {LLM}s through Token Internal Structure Learning",
author = "Xu, Zhu and
Zhao, Zhiqiang and
Zhang, Zihan and
Liu, Yuchi and
Shen, Quanwei and
Liu, Fei and
Kuang, Yu and
He, Jian and
Liu, Conglin",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.194/",
doi = "10.18653/v1/2025.acl-long.194",
pages = "3839--3853",
ISBN = "979-8-89176-251-0",
abstract = "Tokenization methods like Byte-Pair Encoding (BPE) enhance computational efficiency in large language models (LLMs) but often obscure internal character structures within tokens. This limitation hinders LLMs' ability to predict precise character positions, which is crucial in tasks like Chinese Spelling Correction (CSC) where identifying the positions of misspelled characters accelerates correction processes. We propose Token Internal Position Awareness (TIPA), a method that significantly improves models' ability to capture character positions within tokens by training them on reverse character prediction tasks using the tokenizer{'}s vocabulary. Experiments demonstrate that TIPA enhances position prediction accuracy in LLMs, enabling more precise identification of target characters in original text. Furthermore, when applied to downstream tasks that do not require exact position prediction, TIPA still boosts performance in tasks needing character-level information, validating its versatility and effectiveness."
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<abstract>Tokenization methods like Byte-Pair Encoding (BPE) enhance computational efficiency in large language models (LLMs) but often obscure internal character structures within tokens. This limitation hinders LLMs’ ability to predict precise character positions, which is crucial in tasks like Chinese Spelling Correction (CSC) where identifying the positions of misspelled characters accelerates correction processes. We propose Token Internal Position Awareness (TIPA), a method that significantly improves models’ ability to capture character positions within tokens by training them on reverse character prediction tasks using the tokenizer’s vocabulary. Experiments demonstrate that TIPA enhances position prediction accuracy in LLMs, enabling more precise identification of target characters in original text. Furthermore, when applied to downstream tasks that do not require exact position prediction, TIPA still boosts performance in tasks needing character-level information, validating its versatility and effectiveness.</abstract>
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%0 Conference Proceedings
%T Enhancing Character-Level Understanding in LLMs through Token Internal Structure Learning
%A Xu, Zhu
%A Zhao, Zhiqiang
%A Zhang, Zihan
%A Liu, Yuchi
%A Shen, Quanwei
%A Liu, Fei
%A Kuang, Yu
%A He, Jian
%A Liu, Conglin
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F xu-etal-2025-enhancing-character
%X Tokenization methods like Byte-Pair Encoding (BPE) enhance computational efficiency in large language models (LLMs) but often obscure internal character structures within tokens. This limitation hinders LLMs’ ability to predict precise character positions, which is crucial in tasks like Chinese Spelling Correction (CSC) where identifying the positions of misspelled characters accelerates correction processes. We propose Token Internal Position Awareness (TIPA), a method that significantly improves models’ ability to capture character positions within tokens by training them on reverse character prediction tasks using the tokenizer’s vocabulary. Experiments demonstrate that TIPA enhances position prediction accuracy in LLMs, enabling more precise identification of target characters in original text. Furthermore, when applied to downstream tasks that do not require exact position prediction, TIPA still boosts performance in tasks needing character-level information, validating its versatility and effectiveness.
%R 10.18653/v1/2025.acl-long.194
%U https://aclanthology.org/2025.acl-long.194/
%U https://doi.org/10.18653/v1/2025.acl-long.194
%P 3839-3853
Markdown (Informal)
[Enhancing Character-Level Understanding in LLMs through Token Internal Structure Learning](https://aclanthology.org/2025.acl-long.194/) (Xu et al., ACL 2025)
ACL
- Zhu Xu, Zhiqiang Zhao, Zihan Zhang, Yuchi Liu, Quanwei Shen, Fei Liu, Yu Kuang, Jian He, and Conglin Liu. 2025. Enhancing Character-Level Understanding in LLMs through Token Internal Structure Learning. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3839–3853, Vienna, Austria. Association for Computational Linguistics.