@inproceedings{vykopal-etal-2025-soft,
title = "Soft Language Prompts for Language Transfer",
author = "Vykopal, Ivan and
Ostermann, Simon and
Simko, Marian",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.517/",
doi = "10.18653/v1/2025.naacl-long.517",
pages = "10294--10313",
ISBN = "979-8-89176-189-6",
abstract = "Cross-lingual knowledge transfer, especially between high- and low-resource languages, remains challenging in natural language processing (NLP). This study offers insights for improving cross-lingual NLP applications through the combination of parameter-efficient fine-tuning methods. We systematically explore strategies for enhancing cross-lingual transfer through the incorporation of language-specific and task-specific adapters and soft prompts. We present a detailed investigation of various combinations of these methods, exploring their efficiency across 16 languages, focusing on 10 mid- and low-resource languages. We further present to our knowledge the first use of soft prompts for language transfer, a technique we call soft language prompts. Our findings demonstrate that in contrast to claims of previous work, a combination of language and task adapters does not always work best; instead, combining a soft language prompt with a task adapter outperforms most configurations in many cases."
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%0 Conference Proceedings
%T Soft Language Prompts for Language Transfer
%A Vykopal, Ivan
%A Ostermann, Simon
%A Simko, Marian
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F vykopal-etal-2025-soft
%X Cross-lingual knowledge transfer, especially between high- and low-resource languages, remains challenging in natural language processing (NLP). This study offers insights for improving cross-lingual NLP applications through the combination of parameter-efficient fine-tuning methods. We systematically explore strategies for enhancing cross-lingual transfer through the incorporation of language-specific and task-specific adapters and soft prompts. We present a detailed investigation of various combinations of these methods, exploring their efficiency across 16 languages, focusing on 10 mid- and low-resource languages. We further present to our knowledge the first use of soft prompts for language transfer, a technique we call soft language prompts. Our findings demonstrate that in contrast to claims of previous work, a combination of language and task adapters does not always work best; instead, combining a soft language prompt with a task adapter outperforms most configurations in many cases.
%R 10.18653/v1/2025.naacl-long.517
%U https://aclanthology.org/2025.naacl-long.517/
%U https://doi.org/10.18653/v1/2025.naacl-long.517
%P 10294-10313
Markdown (Informal)
[Soft Language Prompts for Language Transfer](https://aclanthology.org/2025.naacl-long.517/) (Vykopal et al., NAACL 2025)
ACL
- Ivan Vykopal, Simon Ostermann, and Marian Simko. 2025. Soft Language Prompts for Language Transfer. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 10294–10313, Albuquerque, New Mexico. Association for Computational Linguistics.