125 citations · 292 across the 19 of their papers we have counts for
39 papers · 1 filter
The Emergent Symbolic Structure of Artificial Neural Networks
R. Thomas McCoy, Paul Soulos, Tal Linzen +1
Modern systems in artificial intelligence (AI) somehow excel in domains for which they seem poorly suited. Intelligence has traditionally been modeled as operating over structured…
Manipulating language models' training data to study syntactic constraint learning: the case of English passivization
Cara Su-Yi Leong, Tal Linzen
Grammatical rules in natural languages are often characterized by exceptions. How do language learners learn these exceptions to otherwise general patterns? Here, we study this que…
SPAWNing Structural Priming Predictions from a Cognitively Motivated Parser
Grusha Prasad, Tal Linzen
Structural priming is a widely used psycholinguistic paradigm to study human sentence representations. In this work we introduce SPAWN, a cognitively motivated parser that can gene…
Do Language Models' Words Refer?
Matthew Mandelkern, Tal Linzen
What do language models (LMs) do with language? Everyone agrees that they can produce sequences of (mostly) coherent strings of English. But do those sentences mean something, or a…
Causal Analysis of Syntactic Agreement Neurons in Multilingual Language Models
Aaron Mueller, Yu Xia, Tal Linzen
Structural probing work has found evidence for latent syntactic information in pre-trained language models. However, much of this analysis has focused on monolingual models, and an…
When a sentence does not introduce a discourse entity, Transformer-based models still sometimes refer to it
Sebastian Schuster, Tal Linzen
Understanding longer narratives or participating in conversations requires tracking of discourse entities that have been mentioned. Indefinite noun phrases (NPs), such as 'a dog',…