2 papers
cs.CL2026
Rethinking the Idiomaticity Decomposability Hypothesis: Evidence from Distributional Learning
Maggie Mi, Golzar Atefi, Atsuki Yamaguchi +3
Idioms can be analysed in terms of their decomposability, the extent to which constituent meanings contribute to the figurative whole. Decomposability is thought to predict syntact…
cs.LG2026
Robust Weight Imprinting: Insights from Neural Collapse and Proxy-Based Aggregation
Justus Westerhoff, Golzar Atefi, Mario Koddenbrock +4
The capacity of foundation models allows for their application to new, unseen tasks. The adaptation to such tasks is called transfer learning. An efficient transfer learning method…