Statement-Tuning Enables Efficient Cross-lingual Generalization in Encoder-only Models

• URL: http://arxiv.org/abs/2506.01592v1
• Date: Mon, 02 Jun 2025 12:28:03 GMT
• Title: Statement-Tuning Enables Efficient Cross-lingual Generalization in Encoder-only Models
• Authors: Ahmed Elshabrawy, Thanh-Nhi Nguyen, Yeeun Kang, Lihan Feng, Annant Jain, Faadil Abdullah Shaikh, Jonibek Mansurov, Mohamed Fazli Mohamed Imam, Jesus-German Ortiz-Barajas, Rendi Chevi, Alham Fikri Aji,
• Abstract summary: Large Language Models (LLMs) excel in zero-shot and few-shot tasks, but achieving similar performance with encoder-only models has been challenging.<n>Recent work adapts them for zero-shot generalization using Statement Tuning, which reformulates tasks into finite templates.<n>We extend this approach to multilingual NLP, exploring whether encoders can achieve zero-shot cross-lingual generalization.
• Score: 7.467951065154891
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) excel in zero-shot and few-shot tasks, but achieving similar performance with encoder-only models like BERT and RoBERTa has been challenging due to their architecture. However, encoders offer advantages such as lower computational and memory costs. Recent work adapts them for zero-shot generalization using Statement Tuning, which reformulates tasks into finite templates. We extend this approach to multilingual NLP, exploring whether encoders can achieve zero-shot cross-lingual generalization and serve as efficient alternatives to memory-intensive LLMs for low-resource languages. Our results show that state-of-the-art encoder models generalize well across languages, rivaling multilingual LLMs while being more efficient. We also analyze multilingual Statement Tuning dataset design, efficiency gains, and language-specific generalization, contributing to more inclusive and resource-efficient NLP models. We release our code and models.

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