Papers by Josef Kuchař
Pre-trained Language Models Learn Remarkably Accurate Representations of Numbers (2025.emnlp-main)
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| Challenge: | Existing work showed limited success in probing numeric values from models’ representations, indicating that these errors can be attributed to the inherent unreliability of distributionally learned embeddings in representing exact quantities. |
| Approach: | They propose a probing technique that decodes numeric values from input embeddings with near-perfect accuracy across a range of open-source LMs. |
| Outcome: | The proposed probing technique decodes numeric values from input embeddings with near-perfect accuracy across a range of open-source LMs. |
Language Models Learn Universal Representations of Numbers and Here’s Why You Should Care (2026.acl-long)
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Michal Štefánik, Timothee Mickus, Marek Kadlčík, Bertram Højer, Michal Spiegel, Raúl Vázquez, Aman Sinha, Josef Kuchař, Philipp Mondorf, Pontus Stenetorp
| Challenge: | Prior work has shown that large language models (LLMs) often converge to accurate input embedding for numbers, based on sinusoidal representations. |
| Approach: | They show that large language models often converge to accurate input embedding for numbers, based on sinusoidal representations. |
| Outcome: | The proposed representations are strikingly systematic, and are interchangeable in a large swathe of experimental setups. |
Can Out-of-Distribution Evaluations Uncover Reliance on Prediction Shortcuts? A Case Study in Question Answering (2025.findings-emnlp)
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| Challenge: | Existing work assesses models’ generalization capabilities through the lens of performance on out-of-distribution (OOD) datasets. |
| Approach: | They challenge this assumption by comparing OOD evaluations with failure modes documented in existing question-answering (QA) models. |
| Outcome: | The proposed evaluations show that the models' generalization capabilities are under-performing on out-of-distribution datasets, while others are underperforming on in-difference datasets. |