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    <abstract>Human readers can efficiently comprehend scrambled words, a phenomenon known as Typoglycemia, primarily by relying on word form; if word form alone is insufficient, they further utilize contextual cues for interpretation. While advanced large language models (LLMs) exhibit similar abilities, the underlying mechanisms remain unclear. To investigate this, we conduct controlled experiments to analyze the roles of word form and contextual information in semantic reconstruction and examine LLM attention patterns. Specifically, we first propose SemRecScore, a reliable metric to quantify the degree of semantic reconstruction, and validate its effectiveness. Using this metric, we study how word form and contextual information influence LLMs’ semantic reconstruction ability, identifying word form as the core factor in this process. Furthermore, we analyze how LLMs utilize word form and find that they rely on specialized attention heads to extract and process word form information, with this mechanism remaining stable across varying levels of word scrambling. This distinction between LLMs’ fixed attention patterns primarily focused on word form and human readers’ adaptive strategy in balancing word form and contextual information provides insights into enhancing LLM performance by incorporating human-like, context-aware mechanisms. Code is available on: https://github.com/Aurora-cx/TypoLLM.</abstract>
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%0 Conference Proceedings
%T Word Form Matters: LLMs’ Semantic Reconstruction under Typoglycemia
%A Wang, Chenxi
%A Gu, Tianle
%A Wei, Zhongyu
%A Gao, Lang
%A Song, Zirui
%A Chen, Xiuying
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F wang-etal-2025-word
%X Human readers can efficiently comprehend scrambled words, a phenomenon known as Typoglycemia, primarily by relying on word form; if word form alone is insufficient, they further utilize contextual cues for interpretation. While advanced large language models (LLMs) exhibit similar abilities, the underlying mechanisms remain unclear. To investigate this, we conduct controlled experiments to analyze the roles of word form and contextual information in semantic reconstruction and examine LLM attention patterns. Specifically, we first propose SemRecScore, a reliable metric to quantify the degree of semantic reconstruction, and validate its effectiveness. Using this metric, we study how word form and contextual information influence LLMs’ semantic reconstruction ability, identifying word form as the core factor in this process. Furthermore, we analyze how LLMs utilize word form and find that they rely on specialized attention heads to extract and process word form information, with this mechanism remaining stable across varying levels of word scrambling. This distinction between LLMs’ fixed attention patterns primarily focused on word form and human readers’ adaptive strategy in balancing word form and contextual information provides insights into enhancing LLM performance by incorporating human-like, context-aware mechanisms. Code is available on: https://github.com/Aurora-cx/TypoLLM.
%R 10.18653/v1/2025.findings-acl.866
%U https://aclanthology.org/2025.findings-acl.866/
%U https://doi.org/10.18653/v1/2025.findings-acl.866
%P 16870-16885

Markdown (Informal)

[Word Form Matters: LLMs’ Semantic Reconstruction under Typoglycemia](https://aclanthology.org/2025.findings-acl.866/) (Wang et al., Findings 2025)

• Word Form Matters: LLMs’ Semantic Reconstruction under Typoglycemia (Wang et al., Findings 2025)

ACL

• Chenxi Wang, Tianle Gu, Zhongyu Wei, Lang Gao, Zirui Song, and Xiuying Chen. 2025. Word Form Matters: LLMs’ Semantic Reconstruction under Typoglycemia. In Findings of the Association for Computational Linguistics: ACL 2025, pages 16870–16885, Vienna, Austria. Association for Computational Linguistics.