1 citations · 3 across the 7 of their papers we have counts for
8 papers
Cost-Efficient Estimation of General Abilities Across Benchmarks
Michael Krumdick, Adam Wiemerslage, Seth Ebner +2
Thousands of diverse benchmarks have been developed to measure the quality of large language models (LLMs). Yet prior work has demonstrated that LLM performance is often sufficient…
The Effect of Scripts and Formats on LLM Numeracy
Varshini Reddy, Craig W. Schmidt, Seth Ebner +3
Large language models (LLMs) have achieved impressive proficiency in basic arithmetic, rivaling human-level performance on standard numerical tasks. However, little attention has b…
Model-Based Ranking of Source Languages for Zero-Shot Cross-Lingual Transfer
Abteen Ebrahimi, Adam Wiemerslage, Katharina von der Wense
We present NN-Rank, an algorithm for ranking source languages for cross-lingual transfer, which leverages hidden representations from multilingual models and unlabeled target-langu…
Improving Low-Resource Morphological Inflection via Self-Supervised Objectives
Adam Wiemerslage, Katharina von der Wense
Self-supervised objectives have driven major advances in NLP by leveraging large-scale unlabeled data, but such resources are scarce for many of the world's languages. Surprisingly…
An Investigation of Noise in Morphological Inflection
Adam Wiemerslage, Changbing Yang, Garrett Nicolai +2
With a growing focus on morphological inflection systems for languages where high-quality data is scarce, training data noise is a serious but so far largely ignored concern. We ai…
A Comprehensive Comparison of Neural Networks as Cognitive Models of Inflection
Adam Wiemerslage, Shiran Dudy, Katharina Kann
Neural networks have long been at the center of a debate around the cognitive mechanism by which humans process inflectional morphology. This debate has gravitated into NLP by way…