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Fine-Tuning Qwen3 Models for the Legal Domain of Kazakhstan: A Comparative Study of LoRA-Adapted Models for Bilingual Legal Question Answering
artificial-intelligencecomputer-sciencejurisdictionnatural-language-processingquestion-answeringsimilarity-geometry
This paper reports a systematic study of low-rank adaptation (LoRA)-based fine-tuning applied to Qwen3 language models (4B, 8B, and 14B parameters) for the task of legal question answering within the jurisdiction of the Republic of Kazakhstan. The bilingual dataset comprises 63,114 question–answer p
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