Word Recovery in Large Language Models Enables Character-Level Tokenization Robustness

Abstract:Large language models (LLMs) trained with canonical tokenization exhibit surprising robustness to non-canonical inputs such as character-level tokenization, yet the mechanisms underlying this robustness remain unclear. We study this phenomenon through mechanistic interpretability and identify a core process we term word recovery. We first introduce a decoding-based method to detect word recovery, showing that hidden states reconstruct canonical word-level token identities from character-level inputs. We then provide causal evidence by removing the corresponding subspace from hidden states, which consistently degrades downstream task performance. Finally, we conduct a fine-grained attention analysis and show that in-group attention among characters belonging to the same canonical token is critical for word recovery: masking such attention in early layers substantially reduces both recovery scores and task performance. Together, our findings provide a mechanistic explanation for tokenization robustness and identify word recovery as a key mechanism enabling LLMs to process character-level inputs.

Computation and Language (cs.CL)

arXiv:2603.10771 [cs.CL]

(or arXiv:2603.10771v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2603.10771 Focus to learn more arXiv-issued DOI via DataCite

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