Impact of LoRA Layer Variation on Cross-Lingual Lexical Alignment in Lugha-Llama for African Languages

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: What is the impact of varying the number of LoRA layers on cross-lingual lexical alignment in Lugha-Llama when benchmarked against the FLORES-200 evaluation suite for diverse African languages?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.6/10.