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cs.CL2025
Language Models Learn Universal Representations of Numbers and Here's Why You Should Care
Michal Štefánik, Timothee Mickus, Marek Kadlčík +7
Prior work has shown that large language models (LLMs) often converge to accurate input embedding for numbers, based on sinusoidal representations. In this work, we quantify that t…
cs.CL2025
Can Out-of-Distribution Evaluations Uncover Reliance on Shortcuts? A Case Study in Question Answering
Michal Štefánik, Timothee Mickus, Marek Kadlčík +2
A majority of recent work in AI assesses models' generalization capabilities through the lens of performance on out-of-distribution (OOD) datasets. Despite their practicality, such…
cs.CL2025
Pre-trained Language Models Learn Remarkably Accurate Representations of Numbers
Marek Kadlčík, Michal Štefánik, Timothee Mickus +2
Pretrained language models (LMs) are prone to arithmetic errors. Existing work showed limited success in probing numeric values from models' representations, indicating that these…