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Early-Layer LoRA Fine-Tuning vs Full-Parameter Tuning for Zero-Shot Cross-Lingual Retrieval in Low-Resource Languages

Domaine:

natural language processing

Type de record:

paper

Créateur:

Ass

Éditeur:

Zenodo

Hôte:

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 in Lugha-Llama compare to full-parameter fine-tuning on zero-shot cross-lingual retrieval accuracy for Swahili and other low-resource languages? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.7/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 7.7/10.

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Tasks

information retrieval

Languages

Tags

early-layer LoRA fine-tuning lexical alignment Lugha-Llama full-parameter zero-shot

Licenses

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

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