@inproceedings{banerjee-etal-2025-breaking,
title = "Breaking Boundaries: Investigating the Effects of Model Editing on Cross-linguistic Performance",
author = "Banerjee, Somnath and
Halder, Avik and
Mandal, Rajarshi and
Layek, Sayan and
Soboroff, Ian and
Hazra, Rima and
Mukherjee, Animesh",
editor = "Chen, Weizhu and
Yang, Yi and
Kachuee, Mohammad and
Fu, Xue-Yong",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-industry.17/",
doi = "10.18653/v1/2025.naacl-industry.17",
pages = "194--209",
ISBN = "979-8-89176-194-0",
abstract = "Pretrained language models (PLMs) have revolutionized NLP but amplify linguistic inequities in multilingual applications. While prior studies focused on transformer architectures such as BERT, we evaluate large language models (LLMs) including Mistral, TowerInstruct, OpenHathi, Tamil-Llama, and Kan-Llama. Through rigorous testing across eight languages spanning high-resource (English, German, French, Italian, Spanish) and low-resource (Hindi, Tamil, Kannada) settings, we reveal systemic failures in preserving multilingual reliability and adaptability. Using paradigms like each language for itself' (ELFI) and each language for others' (ELFO), we highlight the inability of current LLMs to bridge linguistic divides. Even model merging fail to mitigate these gaps, exposing fundamental limitations. These findings emphasize the critical need for reimagining AI architectures to deliver true linguistic inclusivity and equitable performance across diverse languages."
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<abstract>Pretrained language models (PLMs) have revolutionized NLP but amplify linguistic inequities in multilingual applications. While prior studies focused on transformer architectures such as BERT, we evaluate large language models (LLMs) including Mistral, TowerInstruct, OpenHathi, Tamil-Llama, and Kan-Llama. Through rigorous testing across eight languages spanning high-resource (English, German, French, Italian, Spanish) and low-resource (Hindi, Tamil, Kannada) settings, we reveal systemic failures in preserving multilingual reliability and adaptability. Using paradigms like each language for itself’ (ELFI) and each language for others’ (ELFO), we highlight the inability of current LLMs to bridge linguistic divides. Even model merging fail to mitigate these gaps, exposing fundamental limitations. These findings emphasize the critical need for reimagining AI architectures to deliver true linguistic inclusivity and equitable performance across diverse languages.</abstract>
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%0 Conference Proceedings
%T Breaking Boundaries: Investigating the Effects of Model Editing on Cross-linguistic Performance
%A Banerjee, Somnath
%A Halder, Avik
%A Mandal, Rajarshi
%A Layek, Sayan
%A Soboroff, Ian
%A Hazra, Rima
%A Mukherjee, Animesh
%Y Chen, Weizhu
%Y Yang, Yi
%Y Kachuee, Mohammad
%Y Fu, Xue-Yong
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-194-0
%F banerjee-etal-2025-breaking
%X Pretrained language models (PLMs) have revolutionized NLP but amplify linguistic inequities in multilingual applications. While prior studies focused on transformer architectures such as BERT, we evaluate large language models (LLMs) including Mistral, TowerInstruct, OpenHathi, Tamil-Llama, and Kan-Llama. Through rigorous testing across eight languages spanning high-resource (English, German, French, Italian, Spanish) and low-resource (Hindi, Tamil, Kannada) settings, we reveal systemic failures in preserving multilingual reliability and adaptability. Using paradigms like each language for itself’ (ELFI) and each language for others’ (ELFO), we highlight the inability of current LLMs to bridge linguistic divides. Even model merging fail to mitigate these gaps, exposing fundamental limitations. These findings emphasize the critical need for reimagining AI architectures to deliver true linguistic inclusivity and equitable performance across diverse languages.
%R 10.18653/v1/2025.naacl-industry.17
%U https://aclanthology.org/2025.naacl-industry.17/
%U https://doi.org/10.18653/v1/2025.naacl-industry.17
%P 194-209
Markdown (Informal)
[Breaking Boundaries: Investigating the Effects of Model Editing on Cross-linguistic Performance](https://aclanthology.org/2025.naacl-industry.17/) (Banerjee et al., NAACL 2025)
ACL
- Somnath Banerjee, Avik Halder, Rajarshi Mandal, Sayan Layek, Ian Soboroff, Rima Hazra, and Animesh Mukherjee. 2025. Breaking Boundaries: Investigating the Effects of Model Editing on Cross-linguistic Performance. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track), pages 194–209, Albuquerque, New Mexico. Association for Computational Linguistics.