Early-Layer LoRA Adaptation vs Full Fine-Tuning for Zero-Shot Cross-Lingual Transfer in Low-Resource 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: How does early-layer LoRA adaptation for lexical alignment in Lugha-Llama compare to full fine-tuning in zero-shot cross-lingual transfer accuracy on the XNLI dataset for low-resource African languages?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.7/10.