565 citations · 675 across the 33 of their papers we have counts for
4 papers · 1 filter
How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN
R. Thomas McCoy, Paul Smolensky, Tal Linzen +2
Current language models can generate high-quality text. Are they simply copying text they have seen before, or have they learned generalizable linguistic abstractions? To tease apa…
Learning to Generalize Compositionally by Transferring Across Semantic Parsing Tasks
Wang Zhu, Peter Shaw, Tal Linzen +1
Neural network models often generalize poorly to mismatched domains or distributions. In NLP, this issue arises in particular when models are expected to generalize compositionally…
The Language Model Understood the Prompt was Ambiguous: Probing Syntactic Uncertainty Through Generation
Laura Aina, Tal Linzen
Temporary syntactic ambiguities arise when the beginning of a sentence is compatible with multiple syntactic analyses. We inspect to which extent neural language models (LMs) exhib…
The MultiBERTs: BERT Reproductions for Robustness Analysis
Thibault Sellam, Steve Yadlowsky, Jason Wei +9
Experiments with pre-trained models such as BERT are often based on a single checkpoint. While the conclusions drawn apply to the artifact tested in the experiment (i.e., the parti…