Word Frequency Does Not Predict Grammatical Knowledge in Language Models

Abstract

Neural language models learn, to varying degrees of accuracy, the grammatical properties of natural languages. In this work, we investigate whether there are systematic sources of variation in the language models' accuracy. Focusing on subject-verb agreement and reflexive anaphora, we find that certain nouns are systematically understood better than others, an effect which is robust across grammatical tasks and different language models. Surprisingly, we find that across four orders of magnitude, corpus frequency is unrelated to a noun's performance on grammatical tasks. Finally, we find that a novel noun's grammatical properties can be few-shot learned from various types of training data. The results present a paradox: there should be less variation in grammatical performance than is actually observed.

Frequency Effects on Syntactic Rule Learning in Transformers

“…Noun frequency. We find similar evidence, like Yu et al (2020), that BERT did not perform better on nouns that were more frequent in the training set. Figure 8 shows these results-for each subject (we consider both singular and plural forms as a single subject), we plot that subject's error rate against its frequency in the training data.…”

“…There has been substantial prior work on the ability of language models to perform abstract syntactic processing tasks (Hu et al, 2020) (see Linzen and Baroni (2020) for a review). On SVA specifically, Goldberg (2019) found that BERT achieves high accuracy on both natural sentences (97%) and nonce sentences (83%), and that error rate was independent of the number of "distractor" words between the subject and verb; Yu et al (2020) showed that language models do not exhibit better grammatical knowledge of more frequent nouns. Other work has found that BERT's performance is sensitive to factors that may suggest item-specific learning; Chaves and Richter (2021) found that BERT's performance on number agreement is sensitive to the verb, across seven different verbs, and Newman et al (2021) found that language models performed better on verbs that they predicted were likely in context.…”

Refining Targeted Syntactic Evaluation of Language Models

“…Similarly, we observe that models succeed on our MW metric indicating that they correctly inflect verbs with high in-context probability under the model. Relatedly, Yu et al (2020) investigate the nouns used in TSE minimal pairs and find that language model performance at subject-verb number agreement is uncorrelated with unigram probability of the noun. We instead focus on model-estimated in-context probability of the verb in minimal pairs, finding that model performance increases with the model probability.…”

Frequency Effects on Syntactic Rule Learning in Transformers

“…There has been substantial prior work on the ability of language models to perform abstract syntactic processing tasks (Hu et al, 2020) (see Linzen and Baroni (2020) for a review). On SVA specifically, Goldberg (2019) found that BERT achieves high accuracy on both natural sentences (97%) and nonce sentences (83%), and that error rate was independent of the number of "distractor" words between the subject and verb; Yu et al (2020) showed that language models do not exhibit better grammatical knowledge of more frequent nouns. Other work has found that BERT's performance is sensitive to factors that may suggest item-specific learning; Chaves and Richter (2021) found that BERT's performance on number agreement is sensitive to the verb, across seven different verbs, and Newman et al (2021) found that language models performed better on verbs that they predicted were likely in context.…”