Word Form Matters: LLMs' Semantic Reconstruction under Typoglycemia
Authors: name: Chenxi Wang, name: Tianle Gu, name: Zhongyu Wei, name: Lang Gao, name: Zirui Song, name: Xiuying Chen

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.