SEMJ is a self-evolving multilingual LLM judge that turns cross-lingual inconsistency into iterative self-reflection, outperforming voting and reflection baselines on accuracy and consistency.
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QLoRA on Mistral-7B and Phi-2 yields perplexity 3.79-3.81 on Bashkir, close to full fine-tuning's 3.34 but with over 40x fewer trainable parameters, though some base models degrade sharply and best-perplexity models often switch to English in generation.
citing papers explorer
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When Languages Disagree: Self-Evolving Multilingual LLM Judges
SEMJ is a self-evolving multilingual LLM judge that turns cross-lingual inconsistency into iterative self-reflection, outperforming voting and reflection baselines on accuracy and consistency.
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Adapting Large Language Models to a Low-Resource Agglutinative Language: A Comparative Study of LoRA and QLoRA for Bashkir
QLoRA on Mistral-7B and Phi-2 yields perplexity 3.79-3.81 on Bashkir, close to full fine-tuning's 3.34 but with over 40x fewer trainable parameters, though some base models degrade sharply and best-perplexity models often switch to English in generation.