Spelling convention sensitivity in neural language models
This addresses the problem of understanding model sensitivity to surface-level linguistic patterns for researchers in NLP, but it is incremental as it builds on existing probing methods without major breakthroughs.
The study investigated whether large neural language models learn long-distance dependencies in British versus American spelling conventions from training data, finding that models like T5 and GPT2 internalize consistency only for observed lexical items, not nonce words, and remain limited even after debiasing fine-tuning.
We examine whether large neural language models, trained on very large collections of varied English text, learn the potentially long-distance dependency of British versus American spelling conventions, i.e., whether spelling is consistently one or the other within model-generated strings. In contrast to long-distance dependencies in non-surface underlying structure (e.g., syntax), spelling consistency is easier to measure both in LMs and the text corpora used to train them, which can provide additional insight into certain observed model behaviors. Using a set of probe words unique to either British or American English, we first establish that training corpora exhibit substantial (though not total) consistency. A large T5 language model does appear to internalize this consistency, though only with respect to observed lexical items (not nonce words with British/American spelling patterns). We further experiment with correcting for biases in the training data by fine-tuning T5 on synthetic data that has been debiased, and find that finetuned T5 remains only somewhat sensitive to spelling consistency. Further experiments show GPT2 to be similarly limited.