@inproceedings{bellier-etal-2026-soft,
title = "Soft Prompts for Adapting {LLM}s to Cultural Commonsense Knowledge",
author = "Bellier, Gabrielle Le and
Carpuat, Marine and
Sagot, Beno{\^i}t and
Clavel, Chlo{\'e}",
editor = "Prabhakaran, Vinodkumar and
Dev, Sunipa and
Benotti, Luciana and
Hershcovich, Daniel and
Cao, Yong and
Zhou, Li and
Ma, BOlei and
Adebara, Ife",
booktitle = "Proceedings of the 4th Workshop on Cross-Cultural Considerations in {NLP} ({C}3{NLP} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.c3nlp-1.6/",
pages = "76--100",
ISBN = "979-8-89176-420-0",
abstract = "Large Language Models (LLMs) show unbalanced knowledge of cultures across the globe, favoring high-resource cultures over low-resource ones. A possible way to tackle this issue is to fine-tune LLMs on culturally specific data. However, fine-tuning recent LLMs requires high computational resources as well as memory storage, which triggered the development of parameter-efficient fine-tuning (PEFT) approaches, the most widespread being LoRA. In this article, we investigate the use of another class of PEFT approaches, namely soft prompt methods (prompt-tuning and prefix-tuning), to improve LLMs' cultural knowledge across diverse cultures. We focus on cultural alignment on Multiple-Choice Questions of cultural commonsense knowledge. On this task with limited fine-tuning data, we show that soft-prompt-based methods outperform LoRA in comparable settings. Moreover, the trained soft prompts are interpretable and capture similarities between cultures."
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<abstract>Large Language Models (LLMs) show unbalanced knowledge of cultures across the globe, favoring high-resource cultures over low-resource ones. A possible way to tackle this issue is to fine-tune LLMs on culturally specific data. However, fine-tuning recent LLMs requires high computational resources as well as memory storage, which triggered the development of parameter-efficient fine-tuning (PEFT) approaches, the most widespread being LoRA. In this article, we investigate the use of another class of PEFT approaches, namely soft prompt methods (prompt-tuning and prefix-tuning), to improve LLMs’ cultural knowledge across diverse cultures. We focus on cultural alignment on Multiple-Choice Questions of cultural commonsense knowledge. On this task with limited fine-tuning data, we show that soft-prompt-based methods outperform LoRA in comparable settings. Moreover, the trained soft prompts are interpretable and capture similarities between cultures.</abstract>
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%0 Conference Proceedings
%T Soft Prompts for Adapting LLMs to Cultural Commonsense Knowledge
%A Bellier, Gabrielle Le
%A Carpuat, Marine
%A Sagot, Benoît
%A Clavel, Chloé
%Y Prabhakaran, Vinodkumar
%Y Dev, Sunipa
%Y Benotti, Luciana
%Y Hershcovich, Daniel
%Y Cao, Yong
%Y Zhou, Li
%Y Ma, BOlei
%Y Adebara, Ife
%S Proceedings of the 4th Workshop on Cross-Cultural Considerations in NLP (C3NLP 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-420-0
%F bellier-etal-2026-soft
%X Large Language Models (LLMs) show unbalanced knowledge of cultures across the globe, favoring high-resource cultures over low-resource ones. A possible way to tackle this issue is to fine-tune LLMs on culturally specific data. However, fine-tuning recent LLMs requires high computational resources as well as memory storage, which triggered the development of parameter-efficient fine-tuning (PEFT) approaches, the most widespread being LoRA. In this article, we investigate the use of another class of PEFT approaches, namely soft prompt methods (prompt-tuning and prefix-tuning), to improve LLMs’ cultural knowledge across diverse cultures. We focus on cultural alignment on Multiple-Choice Questions of cultural commonsense knowledge. On this task with limited fine-tuning data, we show that soft-prompt-based methods outperform LoRA in comparable settings. Moreover, the trained soft prompts are interpretable and capture similarities between cultures.
%U https://aclanthology.org/2026.c3nlp-1.6/
%P 76-100
Markdown (Informal)
[Soft Prompts for Adapting LLMs to Cultural Commonsense Knowledge](https://aclanthology.org/2026.c3nlp-1.6/) (Bellier et al., C3NLP 2026)
ACL
- Gabrielle Le Bellier, Marine Carpuat, Benoît Sagot, and Chloé Clavel. 2026. Soft Prompts for Adapting LLMs to Cultural Commonsense Knowledge. In Proceedings of the 4th Workshop on Cross-Cultural Considerations in NLP (C3NLP 2026), pages 76–100, San Diego, California, United States. Association for Computational Linguistics.