5 papers
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…
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…
CliniBench: A Clinical Outcome Prediction Benchmark for Generative and Encoder-Based Language Models
Paul Grundmann, Dennis Fast, Jan Frick +4
With their growing capabilities, generative large language models (LLMs) are being increasingly investigated for complex medical tasks. However, their effectiveness in real-world c…
"Where does it hurt?" -- Dataset and Study on Physician Intent Trajectories in Doctor Patient Dialogues
Tom Röhr, Soumyadeep Roy, Fares Al Mohamad +4
In a doctor-patient dialogue, the primary objective of physicians is to diagnose patients and propose a treatment plan. Medical doctors guide these conversations through targeted q…
Comply: Learning Sentences with Complex Weights inspired by Fruit Fly Olfaction
Alexei Figueroa, Justus Westerhoff, Golzar Atefi +5
Biologically inspired neural networks offer alternative avenues to model data distributions. FlyVec is a recent example that draws inspiration from the fruit fly's olfactory circui…