Comparative Analysis of Targeted Lexical Injection and Few-Shot Fine-Tuning on XNLI and TYDI QA Benchmarks

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 the combination of Targeted Lexical Injection with few-shot fine-tuning compare to zero-shot cross-lingual performance on XNLI and TYDI QA benchmarks when evaluated using F1 and accuracy metrics?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.5/10.