Home
Atlas
Insights
Docs
Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

Early-Layer LoRA Fine-Tuning for Cross-Domain Alignment in Lugha-Llama for Low-Resource Bantu Languages

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: Can TLI early-layer LoRA fine-tuning improve cross-domain alignment in Lugha-Llama for low-resource Bantu languages, as evaluated by mAP scores on cross-lingual information retrieval tasks? 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.

Visit

Tasks

information retrieval transfer learning

Languages

Tags

TLI early-layer LoRA fine-tuning improve cross-domain alignment Lugha-Llama

Licenses

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

Similar

Early-Layer LoRA Fine-Tuning with Targeted Lexical Injection for Robustness in Low-Resource Lugha-LlamaLoRA Rank Variation in Early-Layer Fine-Tuning and Cross-Lingual Alignment for Low-Resource African LanguagesEarly-Layer LoRA Fine-Tuning for Lexical Alignment in Lugha-Llama: Zero-Shot Cross-Lingual Transfer Accuracy on XNLI forEarly-Layer LoRA Fine-Tuning for Lexical Alignment in Lugha-Llama and Cross-Lingual Retrieval Accuracy on Swahili BenchmarksTargeted Lexical Injection: Unlocking Latent Cross-Lingual Alignment in Lugha-Llama via Early-Layer LoRA Fine-TuningEarly-Layer LoRA Fine-Tuning vs Full-Parameter Tuning for Cross-Lingual Alignment in Lugha-Llama on XNLI Accuracy in Yoruba and

Early-Layer LoRA Fine-Tuning with Targeted Lexical Injection for Robustness in Low-Resource Lugha-Llama

Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their performance in low

LoRA Rank Variation in Early-Layer Fine-Tuning and Cross-Lingual Alignment for Low-Resource African Languages

Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their performance in low

Early-Layer LoRA Fine-Tuning for Lexical Alignment in Lugha-Llama: Zero-Shot Cross-Lingual Transfer Accuracy on XNLI for

Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their performance in low

Early-Layer LoRA Fine-Tuning for Lexical Alignment in Lugha-Llama and Cross-Lingual Retrieval Accuracy on Swahili Benchmarks

Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their performance in low

Targeted Lexical Injection: Unlocking Latent Cross-Lingual Alignment in Lugha-Llama via Early-Layer LoRA Fine-Tuning

Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their performance in low

Early-Layer LoRA Fine-Tuning vs Full-Parameter Tuning for Cross-Lingual Alignment in Lugha-Llama on XNLI Accuracy in Yoruba and

Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their performance in low