47 citations · 262 across the 33 of their papers we have counts for
5 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…
Distributed neural encoding of binding to thematic roles
Matthias Lalisse, Paul Smolensky
A framework and method are proposed for the study of constituent composition in fMRI. The method produces estimates of neural patterns encoding complex linguistic structures, under…
Scalable knowledge base completion with superposition memories
Matthias Lalisse, Eric Rosen, Paul Smolensky
We present Harmonic Memory Networks (HMem), a neural architecture for knowledge base completion that models entities as weighted sums of pairwise bindings between an entity's neigh…
Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization
Yichen Jiang, Asli Celikyilmaz, Paul Smolensky +7
Abstractive summarization, the task of generating a concise summary of input documents, requires: (1) reasoning over the source document to determine the salient pieces of informat…
Compositional Processing Emerges in Neural Networks Solving Math Problems
Jacob Russin, Roland Fernandez, Hamid Palangi +4
A longstanding question in cognitive science concerns the learning mechanisms underlying compositionality in human cognition. Humans can infer the structured relationships (e.g., g…