Benchmarking Parameter-Efficient Fine-Tuning of Large Language Models for Low-Resource Tajik Text Generation with the Tajik Web Corpus
We release the Tajik Web Corpus (319k docs, 1.11B chars) and benchmark generative LLMs on prompt continuation in Tajik, a low-resource Cyrillic-script language. Seventeen configurations across nine architectures are evaluated under three fine-tuning strategies: full fine-tuning, LoRA, and QLoRA (ranks 8 and 16). Because perplexity is not directly comparable across model families with different tokenizers, generation quality is assessed through perplexity interpreted within each family, complemented by qualitative analysis performed by a native Tajik speaker. Computational cost is measured via GPU memory and training time. The best quality-cost trade-off is achieved by Mistral 7B with QLoR...
| Authors | Mullosharaf K. Arabov |
|---|---|
| Primary category | cs.CL |
| Published | 5 May 2026 |
| Last updated at source | 10 August 2026 |
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