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Early-Layer LoRA Fine-Tuning for Lexical Alignment in Low-Resource African Languages on XNLI Accuracy

Domain:

natural language processing

Record type:

paper

Creator:

Ass

Publisher:

Zenodo

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 alignment compare to full-parameter fine-tuning on XNLI accuracy for low-resource African languages? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 7.5/10.

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Tasks

natural language inference transfer learning

Languages

Tags

early-layer LoRA fine-tuning lexical alignment full-parameter XNLI accuracy

Licenses

Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode

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