@inproceedings{bui-etal-2025-generalization,
title = "On Generalization across Measurement Systems: {LLM}s Entail More Test-Time Compute for Underrepresented Cultures",
author = "Bui, Minh Duc and
Park, Kyung Eun and
Glava{\v{s}}, Goran and
Schmidt, Fabian David and
Wense, Katharina Von Der",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1032/",
doi = "10.18653/v1/2025.acl-long.1032",
pages = "21262--21276",
ISBN = "979-8-89176-251-0",
abstract = "Measurement systems (e.g., currencies) differ across cultures, but the conversions between them are well defined so that humans can state using any measurement system of their choice. Being available to users from diverse cultural backgrounds, Large Language Models (LLMs) should also be able to provide accurate information irrespective of the measurement system at hand. Using newly compiled datasets we test if this is truly the case for seven open-source LLMs, addressing three key research questions: (RQ1) What is the default system used by LLMs for each type of measurement? (RQ2) Do LLMs' answers and their accuracy vary across different measurement systems? (RQ3) Can LLMs mitigate potential challenges w.r.t. underrepresented systems via reasoning? Our findings show that LLMs default to the measurement system predominantly used in the data. Additionally, we observe considerable instability and variance in performance across different measurement systems. While this instability can in part be mitigated by employing reasoning methods such as chain-of-thought (CoT), this implies longer responses and thereby significantly increases test-time compute (and inference costs), marginalizing users from cultural backgrounds that use underrepresented measurement systems."
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<abstract>Measurement systems (e.g., currencies) differ across cultures, but the conversions between them are well defined so that humans can state using any measurement system of their choice. Being available to users from diverse cultural backgrounds, Large Language Models (LLMs) should also be able to provide accurate information irrespective of the measurement system at hand. Using newly compiled datasets we test if this is truly the case for seven open-source LLMs, addressing three key research questions: (RQ1) What is the default system used by LLMs for each type of measurement? (RQ2) Do LLMs’ answers and their accuracy vary across different measurement systems? (RQ3) Can LLMs mitigate potential challenges w.r.t. underrepresented systems via reasoning? Our findings show that LLMs default to the measurement system predominantly used in the data. Additionally, we observe considerable instability and variance in performance across different measurement systems. While this instability can in part be mitigated by employing reasoning methods such as chain-of-thought (CoT), this implies longer responses and thereby significantly increases test-time compute (and inference costs), marginalizing users from cultural backgrounds that use underrepresented measurement systems.</abstract>
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%0 Conference Proceedings
%T On Generalization across Measurement Systems: LLMs Entail More Test-Time Compute for Underrepresented Cultures
%A Bui, Minh Duc
%A Park, Kyung Eun
%A Glavaš, Goran
%A Schmidt, Fabian David
%A Wense, Katharina Von Der
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F bui-etal-2025-generalization
%X Measurement systems (e.g., currencies) differ across cultures, but the conversions between them are well defined so that humans can state using any measurement system of their choice. Being available to users from diverse cultural backgrounds, Large Language Models (LLMs) should also be able to provide accurate information irrespective of the measurement system at hand. Using newly compiled datasets we test if this is truly the case for seven open-source LLMs, addressing three key research questions: (RQ1) What is the default system used by LLMs for each type of measurement? (RQ2) Do LLMs’ answers and their accuracy vary across different measurement systems? (RQ3) Can LLMs mitigate potential challenges w.r.t. underrepresented systems via reasoning? Our findings show that LLMs default to the measurement system predominantly used in the data. Additionally, we observe considerable instability and variance in performance across different measurement systems. While this instability can in part be mitigated by employing reasoning methods such as chain-of-thought (CoT), this implies longer responses and thereby significantly increases test-time compute (and inference costs), marginalizing users from cultural backgrounds that use underrepresented measurement systems.
%R 10.18653/v1/2025.acl-long.1032
%U https://aclanthology.org/2025.acl-long.1032/
%U https://doi.org/10.18653/v1/2025.acl-long.1032
%P 21262-21276
Markdown (Informal)
[On Generalization across Measurement Systems: LLMs Entail More Test-Time Compute for Underrepresented Cultures](https://aclanthology.org/2025.acl-long.1032/) (Bui et al., ACL 2025)
ACL