LoRA Rank Variation in Middle-Layer vs. Late-Layer Fine-Tuning for Swahili-English Cross-Lingual Alignment
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natural language processingType de record:
paper
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Ass
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Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their performance in low-resource languages (LRLs), such as Swahili, often lags due to data scarcity and underrepresentation in pre-training. A key challenge is achieving robust cross-lingual lexical alignment, crucial for tasks like translation and cross-lingual information retrieval. This paper introduces Targeted Lexical Injection (TLI), a novel and efficient fine-tuning approach. We first demonstrate that Lugha-Llama-8B-wura, a Swahili-centric LLM, exhibits strong, near-perfect lexical alignment for Swahili-English
Research goal: How does varying the LoRA rank in middle-layer versus late-layer fine-tuning impact cross-lingual alignment quality for Swahili-English pairs as measured by CLIR@K on Lugha-Llama?
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