Linear representations of grammaticality in neural language models
Whether neural language models (NLMs) possess the ability to distinguish strings on the basis of their grammaticality remains a debated topic in the computational linguistics literature. Existing evidence has largely relied on probability-based measures, testing whether models assign higher probabilities to grammatical than ungrammatical strings. However, probability comparisons have been criticized as a measure for grammatical knowledge based on the assumption that grammaticality is inherently entangled with likelihood. Model-assigned probability is a function of many related sentence properties, such as lexical frequency, plausibility, and world knowledge. In this work, we move beyond p...
| Authors | Jane Li, Najoung Kim |
|---|---|
| Primary category | cs.CL |
| Published | 16 July 2026 |
| Last updated at source | 16 July 2026 |
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