@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://preview.aclanthology.org/ingest-acl-workshops/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."
}