Large language models (LLMs) have come closest among all models to date to mastering
human language, yet opinions about their linguistic and cognitive capabilities remain split …

Modern deep neural networks achieve impressive performance in engineering applications
that require extensive linguistic skills, such as machine translation. This success has …

In this paper, we experimentally evaluate the zero-shot performance of GPT-4 against prior
generations of GPT on the entire uniform bar examination (UBE), including not only the …

Modern machine learning has subverted and bypassed the theoretical framework of
Chomsky's generative approach to linguistics, including its core claims to particular insights …

Pre-training by language modeling has become a popular and successful approach to NLP
tasks, but we have yet to understand exactly what linguistic capacities these pre-training …

Contextual word representations derived from large-scale neural language models are
successful across a diverse set of NLP tasks, suggesting that they encode useful and …

This paper investigates the ability of artificial neural networks to judge the grammatical
acceptability of a sentence, with the goal of testing their linguistic competence. We introduce …

Abstract We introduce The Benchmark of Linguistic Minimal Pairs (BLiMP), a challenge set
for evaluating the linguistic knowledge of language models (LMs) on major grammatical …

Rapid progress in machine learning for natural language processing has the potential to
transform debates about how humans learn language. However, the learning environments …

We studied the learnability of English filler-gap dependencies and the “island” constraints on
them by assessing the generalizations made by autoregressive (incremental) language …