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Standard pretrained language models operate on sequences of subword tokenswithout direct access to the characters that compose each token's stringrepresentation. We probe the embedding layer of pretrained language models andshow that models learn the internal character composition of whole word andsubword tokens to a surprising extent, without ever seeing the characterscoupled with the tokens. Our results show that the embedding layer of RoBERTaholds enough information to accurately spell up to a third of the vocabularyand reach high average character ngram overlap on all token types. We furthertest whether enriching subword models with additional character information canimprove language modeling, and observe that this method has a near-identicallearning curve as training without spelling-based enrichment. Overall, ourresults suggest that language modeling objectives incentivize the model toimplicitly learn some notion of spelling, and that explicitly teaching themodel how to spell does not appear to enhance its performance on such tasks.

Models In a Spelling Bee: Language Models Implicitly Learn the Character Composition of Tokens