Bridging Language Gaps: Enhancing Few-Shot Language Adaptation

2025-08-26

AI Summary

Low-resource languages suffer from severe performance degradation in multilingual NLP due to scarce annotated data, lagging significantly behind high-resource counterparts. To address this, we propose CoLAP—a novel method that unifies contrastive learning, cross-lingual representation alignment, and prompt-based adaptation for efficient knowledge transfer. CoLAP introduces no additional parameters and is compatible with both encoder-only and decoder-only multilingual large language models. It substantially enhances few-shot cross-lingual generalization without architectural modifications. Empirically, CoLAP achieves state-of-the-art results on natural language inference and relation extraction across multiple low-resource languages, consistently outperforming existing few-shot transfer and in-context learning approaches. Crucially, it narrows the performance gap between low- and high-resource languages while reducing reliance on large-scale labeled datasets.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Language and Vision

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The disparity in language resources poses a challenge in multilingual NLP, with high-resource languages benefiting from extensive data, while low-resource languages lack sufficient data for effective training. Our Contrastive Language Alignment with Prompting (CoLAP) method addresses this gap by integrating contrastive learning with cross-lingual representations, facilitating task-specific knowledge transfer from high-resource to lower-resource languages. The primary advantage of our approach is its data efficiency, enabling rapid adaptation to new languages and reducing the need for large labeled datasets. We conduct experiments with multilingual encoder-only and decoder-only language models on natural language understanding tasks, including natural language inference and relation extraction, evaluating performance across both high- and low-resource languages. Our results demonstrate that CoLAP outperforms few-shot cross-lingual transfer baselines and in-context learning, even with limited available data. This effectively narrows the cross-lingual performance gap, contributing to the development of more efficient multilingual NLP techniques.

Problem

• Addressing resource disparity in multilingual NLP
• Enhancing few-shot cross-lingual knowledge transfer
• Reducing performance gap between high-low resource languages

Innovation

• Contrastive learning with cross-lingual representations integration
• Prompting method for few-shot language adaptation
• Data-efficient transfer from high to low-resource languages