Comparative Analysis of Early versus Middle-Layer LoRA for Cross-Lingual Alignment in Morphologically Rich Languages
Domain:
natural language processingRecord type:
paper
Creator:
SOV
Publisher:
Host:
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 early-layer LoRA fine-tuning for lexical injection compare to middle-layer adaptation in improving cross-lingual alignment scores on mXGLUE for morphologically rich languages?
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