Accueil
Atlas
Analyses
Documentation
Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

LoRA Fine-Tuning Depth and Cross-Lingual Consistency in Low-Resource African Languages

Domaine:

natural language processing

Type de record:

paper

Créateur:

Ass

Éditeur:

Zenodo

Hôte:

This report synthesises findings from 5 peer-reviewed papers addressing the following research question: How does the depth of LoRA fine-tuning affect the cross-lingual consistency of multilingual PLMs on XTREME-R for Swahili compared to other low-resource African languages when evaluated using XLM-R's. 9 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.1/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the depth of LoRA fine-tuning affect the cross-lingual consistency of multilingual PLMs on XTREME-R for Swahili compared to other low-resource African languages when evaluated using XLM-R's accuracy?

Autonomous literature synthesis. Automated review score: 8.1/10. Full text and citation available at Assignee Research.

Machine-generated literature synthesis. Content is derived from peer-reviewed papers; see individual sources for authoritative data. Automated review score: 8.1/10. Published by Assignee Research (

Visit

Tasks

transfer learning

Languages

Tags

depth LoRA fine-tuning affect cross-lingual consistency multilingual PLMs

Licenses

info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode

Similaires

Early-Layer LoRA Fine-Tuning for Zero-Shot Cross-Lingual NLI in Low-Resource African LanguagesLoRA Rank Variation in Early-Layer Fine-Tuning and Cross-Lingual Alignment for Low-Resource African LanguagesEarly-Layer vs. Late-Layer LoRA Fine-Tuning for Cross-Lingual NLI in Low-Resource African LanguagesEarly-Layer LoRA Fine-Tuning vs Full-Parameter Tuning for Zero-Shot Cross-Lingual Retrieval in Low-Resource LanguagesEarly-Layer LoRA Adaptation vs Full Fine-Tuning for Zero-Shot Cross-Lingual Transfer in Low-Resource African LanguagesEarly-Layer LoRA Versus Full-Parameter Fine-Tuning for Cross-Lingual Lexical Alignment in Low-Resource African Languages on

Early-Layer LoRA Fine-Tuning for Zero-Shot Cross-Lingual NLI in Low-Resource African Languages

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 vs. Late-Layer LoRA Fine-Tuning for Cross-Lingual NLI in Low-Resource African Languages

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

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

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

Early-Layer LoRA Adaptation vs Full Fine-Tuning for Zero-Shot Cross-Lingual Transfer in Low-Resource African Languages

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

Early-Layer LoRA Versus Full-Parameter Fine-Tuning for Cross-Lingual Lexical Alignment in Low-Resource African Languages on

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