Parameter-Efficient Multilingual Adaptation of on-Device Language Models: A Survey
Abstract
The rapid integration of language models into smartphones, wearables, vehicles, and Internet of Things devices has intensified demand for multilingual intelligence that operates efficiently, privately, and reliably at the network edge. However, adapting pretrained language models to numerous languages and regional varieties remains difficult because conventional full-parameter fine-tuning requires substantial memory, computation, energy, storage, and communication resources. This review examines parameter-efficient multilingual adaptation techniques for on- device language models, synthesizing advances in adapters, low-rank adaptation, prompt and prefix tuning, bias-only tuning, sparse updates, modular routing, quantization-aware adaptation, and knowledge distillation. It analyzes how these approaches balance adaptation quality, parameter sharing, language specificity, inference latency, energy consumption, and hardware compatibility across high-resource, low-resource, and typologically diverse languages. Particular attention is given to cross-lingual transfer, multilingual interference, tokenizer limitations, script diversity, code-switching, dialectal variation, catastrophic forgetting, and the unequal representation of languages in training and evaluation datasets. The review further discusses deployment workflows involving model compression, federated and continual learning, personalization, privacy preservation, and dynamic selection of language-specific modules under constrained device conditions. Existing evaluation practices are assessed across accuracy, robustness, fairness, memory footprint, throughput, battery usage, and user-centered performance, revealing limited methodological consistency and insufficient testing on realistic edge hardware, thereby preventing reliable comparison across model families, device classes, languages, tasks, and diverse real-world operational settings globally. The survey identifies persistent gaps concerning standardized benchmarks, reproducibility, security, adaptive resource allocation, and support for underserved linguistic communities. It proposes a research agenda centered on hardware-aware multilingual adaptation, composable parameter modules, privacy-preserving updates, inclusive dataset development, and joint optimization of linguistic quality and system efficiency. By connecting algorithmic innovation with deployment realities, this review provides researchers and practitioners with a structured understanding of current methods, trade-offs, and design priorities for building scalable, equitable, and sustainable multilingual language models capable of dependable, independent on-device operation.
Keywords
Cross-lingual transfer; Edge artificial intelligence; Multilingual language models; On- device learning; Parameter-efficient fine-tuning; Resource-constrained computing
References
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