Transferable Modeling Strategies for Low-Resource LLM Tasks: A Prompt and Alignment-Based

2025-07-01

AI Summary

To address the weak cross-lingual transfer and adaptation capability of large language models (LLMs) in low-resource languages, this paper proposes a transferable modeling framework grounded in prompt tuning and knowledge alignment. The framework enforces cross-lingual semantic consistency via a knowledge alignment loss and achieves efficient few-shot adaptation through soft prompt tuning. It incorporates parameter-efficient fine-tuning strategies—including frozen backbone, prompt injection, lightweight adapter modules, and pseudo-data augmentation—to substantially reduce computational overhead. Evaluated on multilingual benchmarks including MLQA, XQuAD, and PAWS-X, our method consistently outperforms existing state-of-the-art approaches. Notably, it demonstrates superior generalization, robustness, and training stability under extremely low-resource settings—e.g., fewer than ten examples per class—highlighting its effectiveness for practical deployment in resource-constrained linguistic environments.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

This paper addresses the limited transfer and adaptation capabilities of large language models in low-resource language scenarios. It proposes a unified framework that combines a knowledge transfer module with parameter-efficient fine-tuning strategies. The method introduces knowledge alignment loss and soft prompt tuning to guide the model in effectively absorbing the structural features of target languages or tasks under minimal annotation. This enhances both generalization performance and training stability. The framework includes lightweight adaptation modules to reduce computational costs. During training, it integrates freezing strategies and prompt injection to preserve the model's original knowledge while enabling quick adaptation to new tasks. The study also conducts stability analysis experiments and synthetic pseudo-data transfer experiments to systematically evaluate the method's applicability and robustness across different low-resource tasks. Experimental results show that compared with existing multilingual pre-trained models and mainstream transfer methods, the proposed approach achieves higher performance and stability on cross-lingual tasks such as MLQA, XQuAD, and PAWS-X. It demonstrates particularly strong advantages under extremely data-scarce conditions. The proposed method offers strong generality and scalability. It enhances task-specific adaptability while preserving the general capabilities of large language models. This makes it well-suited for complex semantic modeling and multilingual processing tasks.

Problem

• Enhancing LLM transfer in low-resource languages
• Combining knowledge transfer with efficient fine-tuning
• Improving generalization and stability with minimal annotation

Innovation

• Combines knowledge transfer with efficient fine-tuning
• Uses alignment loss and soft prompt tuning
• Integrates lightweight modules and freezing strategies