Logo Lanfrica
  • 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.

Scaling Parameter Count in Multilingual Encoders and Recall@k for Low-Resource South Asian Languages

Domaine:

natural language processing

Type de record:

paper
Créateur:
SOV
Éditeur:
Zenodo
Hôte:avatar
Benefiting from transformer-based pre-trained language models, neural ranking models have made significant progress. More recently, the advent of multilingual pre-trained language models provides great support for designing neural cross-lingual retrieval models. However, due to unbalanced pre-training data in different languages, multilingual language models have already shown a performance gap between high and low-resource languages in many downstream tasks. And cross-lingual retrieval models built on such pre-trained models can inherit language bias, leading to suboptimal result for low-reso Research goal: How does scaling parameter count in multilingual encoders affect recall@k for low-resource South Asian languages in cross-lingual retrieval benchmarks? Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 9.3/10. This report was generated autonomously by SOVEREIGN Research Kernel, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 9.3/10.

Visit

doi.orgzenodo.org

Tasks

information retrieval

Tags

scalingparametercountmultilingualencodersaffectrecalllow-resource

Licenses

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

Similaires

Utilizing Multilingual Encoders to Improve Large Language Models for Low-Resource LanguagesParameter Scaling and Robustness in Meta-Learned Few-Shot Classifiers for Low-Resource LanguagesEnhancing Recall@K for Agglutinative Low-Resource African Languages via Phonetic and Morphological Feature Embeddings in DenseMultilingual Auxiliary Task Scaling for Zero-Shot Hate Speech Detection in Low-Resource LanguagesParameter Scaling in Encoder-Only Models for Zero-Shot Cross-Lingual Accuracy on XTREME-R Low-Resource LanguagesScaling Artificially Code-Switched Training Data for Zero-Shot Multilingual Dense Retrieval in Low-Resource Languages

Utilizing Multilingual Encoders to Improve Large Language Models for Low-Resource Languages

Large Language Models (LLMs) excel in English, but their performance degrades significantly on low-r

Parameter Scaling and Robustness in Meta-Learned Few-Shot Classifiers for Low-Resource Languages

State-of-the-art few-shot learning (FSL) methods leverage prompt-based fine-tuning to obtain remarka

Enhancing Recall@K for Agglutinative Low-Resource African Languages via Phonetic and Morphological Feature Embeddings in Dense

Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their performan

Multilingual Auxiliary Task Scaling for Zero-Shot Hate Speech Detection in Low-Resource Languages

The goal of hate speech detection is to filter negative online content aiming at certain groups of p

Parameter Scaling in Encoder-Only Models for Zero-Shot Cross-Lingual Accuracy on XTREME-R Low-Resource Languages

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuni

Scaling Artificially Code-Switched Training Data for Zero-Shot Multilingual Dense Retrieval in Low-Resource Languages

Transferring information retrieval (IR) models from a high-resource language (typically English) to