Small models, many languages: parameter-efficient adaptation of multilingual language models for low-resource Indonesian languages - Ainul Hadziqi (Indonesian Journal of Computational Language Studies, 2026)
Background: Multilingual language models have expanded computational access to many languages, yet Indonesian local languages remain unevenly represented in corpora, benchmarks, and adaptation pipelines despite Indonesia’s exceptional linguistic diversity. Objective: This study examines how parameter-efficient adaptation can support small multilingual language models for low-resource Indonesian languages by treating corpus adequacy, source provenance, language coverage, and evaluation readiness as central methodological conditions. Method: Using a corpus-level research design, this study maps 20 public source-level items consisting of task datasets, benchmark resources, documentation, registry infrastructure, supplementary corpus portals, and model-context collections for Indonesian and selected local languages. Results: The findings show that task-ready datasets form the strongest part of the corpus frame, while benchmarking, reproducibility, and auxiliary corpus layers remain less evenly distributed. Language coverage is stratified, with Javanese and Sundanese occupying stronger cross-task positions, while Acehnese, Ngaju, Bima, Toba Batak, and Ambonese Malay are better treated as focused transfer-stress cases. Implication: These patterns indicate that parameter-efficient fine-tuning should not be interpreted through model efficiency alone, because adaptation gains remain bounded by corpus distribution and language-specific evidence. Novelty: The novelty of this study lies in repositioning PEFT for Indonesian local languages as a corpus-dependent multilingual adaptation problem rather than a purely architectural optimization problem