Are Multilingual Models Improving? Revisiting Cross-Lingual Transfer

Prasoon Bajpai; Eleftheria Briakou; Preethi Jyothi; Vihari Piratla

Are Multilingual Models Improving? Revisiting Cross-Lingual Transfer

Prasoon Bajpai, Eleftheria Briakou, Preethi Jyothi, Vihari Piratla

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Abstract

Cross-lingual transfer is a model’s ability to generalize capabilities from well-represented source languages to under-represented target languages. Existing measures to quantify a model’s transfer strength conflate improvements in transfer with general improvements to accuracy in source language. We advocate an alternate metric that reliably captures transfer strength called Hardness Adjusted Transfer (HAT) Score, and use it to derive multiple insights on factors influencing transfer strength. Our analysis with twenty diverse language models and three popular mainstream multilingual benchmarks argues that 1) transfer in small models is not broken, 2) we are making slower than expected progress with model size, and 3) have made clear progress over time.

Anthology ID:: 2026.findings-emnlp.2388
Volume:: Findings of the Association for Computational Linguistics: EMNLP 2026
Month:: October
Year:: 2026
Address:: Budapest, Hungary
Editors:: Hung-Yi Lee, Sunipa Dev, Jessy Li, Desmond Elliott
Venue:: Findings
SIG:: SIGDAT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 51329–51357
Language:
URL:: https://aclanthology.org/2026.findings-emnlp.2388/
DOI:
Bibkey:
Cite (ACL):: Prasoon Bajpai, Eleftheria Briakou, Preethi Jyothi, and Vihari Piratla. 2026. Are Multilingual Models Improving? Revisiting Cross-Lingual Transfer. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 51329–51357, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):: Are Multilingual Models Improving? Revisiting Cross-Lingual Transfer (Bajpai et al., Findings 2026)
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PDF:: https://aclanthology.org/2026.findings-emnlp.2388.pdf

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@inproceedings{bajpai-etal-2026-multilingual,
    title = "Are Multilingual Models Improving? Revisiting Cross-Lingual Transfer",
    author = "Bajpai, Prasoon  and
      Briakou, Eleftheria  and
      Jyothi, Preethi  and
      Piratla, Vihari",
    editor = "Lee, Hung-Yi  and
      Dev, Sunipa  and
      Li, Jessy  and
      Elliott, Desmond",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-emnlp.2388/",
    pages = "51329--51357",
    ISBN = "979-8-89176-462-0",
    abstract = "Cross-lingual transfer is a model{'}s ability to generalize capabilities from well-represented source languages to under-represented target languages. Existing measures to quantify a model{'}s transfer strength conflate improvements in transfer with general improvements to accuracy in source language. We advocate an alternate metric that reliably captures transfer strength called Hardness Adjusted Transfer (HAT) Score, and use it to derive multiple insights on factors influencing transfer strength. Our analysis with twenty diverse language models and three popular mainstream multilingual benchmarks argues that 1) transfer in small models is not broken, 2) we are making slower than expected progress with model size, and 3) have made clear progress over time."
}

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%0 Conference Proceedings
%T Are Multilingual Models Improving? Revisiting Cross-Lingual Transfer
%A Bajpai, Prasoon
%A Briakou, Eleftheria
%A Jyothi, Preethi
%A Piratla, Vihari
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F bajpai-etal-2026-multilingual
%X Cross-lingual transfer is a model’s ability to generalize capabilities from well-represented source languages to under-represented target languages. Existing measures to quantify a model’s transfer strength conflate improvements in transfer with general improvements to accuracy in source language. We advocate an alternate metric that reliably captures transfer strength called Hardness Adjusted Transfer (HAT) Score, and use it to derive multiple insights on factors influencing transfer strength. Our analysis with twenty diverse language models and three popular mainstream multilingual benchmarks argues that 1) transfer in small models is not broken, 2) we are making slower than expected progress with model size, and 3) have made clear progress over time.
%U https://aclanthology.org/2026.findings-emnlp.2388/
%P 51329-51357

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Markdown (Informal)

[Are Multilingual Models Improving? Revisiting Cross-Lingual Transfer](https://aclanthology.org/2026.findings-emnlp.2388/) (Bajpai et al., Findings 2026)

Are Multilingual Models Improving? Revisiting Cross-Lingual Transfer (Bajpai et al., Findings 2026)

ACL

Prasoon Bajpai, Eleftheria Briakou, Preethi Jyothi, and Vihari Piratla. 2026. Are Multilingual Models Improving? Revisiting Cross-Lingual Transfer. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 51329–51357, Budapest, Hungary. Association for Computational Linguistics.