@inproceedings{wang-etal-2025-word,
    title = "Word Form Matters: {LLM}s' Semantic Reconstruction under Typoglycemia",
    author = "Wang, Chenxi  and
      Gu, Tianle  and
      Wei, Zhongyu  and
      Gao, Lang  and
      Song, Zirui  and
      Chen, Xiuying",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.866/",
    pages = "16870--16885",
    ISBN = "979-8-89176-256-5",
    abstract = "Human readers can efficiently comprehend scrambled words, a phenomenon known as $\textit{Typoglycemia}$, 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 $\textit{SemRecScore}$, 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 $\textit{word form as the core factor}$ in this process. Furthermore, we analyze $\textit{how}$ 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."
}