Parameter-Efficient Fine-Tuning for Multilingual Natural Language Understanding
Keywords:
Parameter-efficient fine-tuning; multilingual natural language understanding; cross-lingual transfer; model governance; low-rank adaptation; linguistic fairnessAbstract
Parameter-efficient fine-tuning has become a decisive architectural and operational strategy for adapting large pretrained language models to multilingual natural language understanding tasks without retraining or storing full model replicas. This paper presents a systems-oriented analysis of parameter-efficient adaptation, focusing on structural trade-offs among adapter modules, low-rank decompositions, prefix-based approaches, and selective parameter updates. The discussion emphasizes cross-lingual transfer dynamics, in which task-specific and language-specific knowledge must be balanced under limited parameter budgets. Rather than treating parameter efficiency solely as a compression problem, the paper examines multilingual adaptation as a socio-technical infrastructure challenge involving deployment constraints, evaluation fairness, energy consumption, and governance. The analysis shows how small parameter updates can preserve strong zero-shot cross-lingual capacity while reducing memory and communication overhead in distributed training and serving environments. The paper also addresses risks of linguistic performance asymmetry, tokenizer-induced cost disparities, and evaluation benchmarks that obscure underperformance in low-resource languages. Through a synthesis of architectural strategies, deployment considerations, and policy implications, the paper argues that parameter-efficient multilingual fine-tuning should be governed by explicit fairness, sustainability, and auditability principles. The analysis contributes a system-level framework for designing multilingual natural language understanding systems that are not only computationally tractable but also institutionally accountable and linguistically inclusive.
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