Papers by Bertram Højer
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. |
Research Community Perspectives on “Intelligence” and Large Language Models (2025.findings-acl)
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| Challenge: | Despite the widespread use of ‘artificial intelligence’ (AI) framing in NLP research, it is not clear what researchers mean by ”intelligence”. |
| Approach: | They propose to use the term "AI" to describe the perception of a system as intelligent, but note that it is not accepted by the majority of respondents. |
| Outcome: | The results suggest that the perception of the current NLP systems as 'intelligent' is a minority position (29%). |