@inproceedings{stefanik-etal-2026-language,
title = "Language Models Learn Universal Representations of Numbers and Here{'}s Why You Should Care",
author = "{\v{S}}tef{\'a}nik, Michal and
Mickus, Timothee and
Kadl{\v{c}}{\'i}k, Marek and
H{\o}jer, Bertram and
Spiegel, Michal and
V{\'a}zquez, Ra{\'u}l and
Sinha, Aman and
Kucha{\v{r}}, Josef and
Mondorf, Philipp and
Stenetorp, Pontus",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1415/",
pages = "30663--30681",
ISBN = "979-8-89176-390-6",
abstract = "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 demonstrate that these representations are in fact strikingly systematic, to the point of being almost perfectly universal: different LLM families develop equivalent sinusoidal structures, and number representations are broadly interchangeable in a large swathe of experimental setups.We show that properly factoring in this characteristic is crucial when it comes to assessing how accurately LLMs encode numeric and other ordinal information, and that mechanistically enhancing this sinusoidality can also lead to reductions of LLMs' arithmetic errors."
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%0 Conference Proceedings
%T Language Models Learn Universal Representations of Numbers and Here’s Why You Should Care
%A Štefánik, Michal
%A Mickus, Timothee
%A Kadlčík, Marek
%A Højer, Bertram
%A Spiegel, Michal
%A Vázquez, Raúl
%A Sinha, Aman
%A Kuchař, Josef
%A Mondorf, Philipp
%A Stenetorp, Pontus
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F stefanik-etal-2026-language
%X 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 demonstrate that these representations are in fact strikingly systematic, to the point of being almost perfectly universal: different LLM families develop equivalent sinusoidal structures, and number representations are broadly interchangeable in a large swathe of experimental setups.We show that properly factoring in this characteristic is crucial when it comes to assessing how accurately LLMs encode numeric and other ordinal information, and that mechanistically enhancing this sinusoidality can also lead to reductions of LLMs’ arithmetic errors.
%U https://aclanthology.org/2026.acl-long.1415/
%P 30663-30681
Markdown (Informal)
[Language Models Learn Universal Representations of Numbers and Here’s Why You Should Care](https://aclanthology.org/2026.acl-long.1415/) (Štefánik et al., ACL 2026)
ACL
- Michal Štefánik, Timothee Mickus, Marek Kadlčík, Bertram Højer, Michal Spiegel, Raúl Vázquez, Aman Sinha, Josef Kuchař, Philipp Mondorf, and Pontus Stenetorp. 2026. Language Models Learn Universal Representations of Numbers and Here’s Why You Should Care. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 30663–30681, San Diego, California, United States. Association for Computational Linguistics.