Fine-Tuning on WebFAQ for Cross-Lingual Semantic Similarity in Low-Resource XNLI Languages

Abstract

Abstract: 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 Question

Does fine-tuning on WebFAQ's diverse FAQ schema lead to measurable improvements in cross-lingual semantic similarity scores on XNLI for languages with fewer than 1 million training samples?

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