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.

SERENGETI: Massively Multilingual Language Models for Africa

Domaine:

natural language processing

Type de record:

paper

Multilingual language models (MLMs) acquire valuable, generalizable linguistic information during pretraining and have advanced the state of the art on task-specific finetuning. So far, only ~ 28 out of ~2,000 African languages are covered in existing language models. We ameliorate this limitation by developing SERENGETI, a set of massively multilingual language model that covers 517 African languages and language varieties. We evaluate our novel models on eight natural language understanding tasks across 20 datasets, comparing to four MLMs that each cover any number of African languages. SERENGETI outperforms other models on 11 datasets across the eights tasks and achieves 82.27 average F-1.

We also perform error analysis on our models' performance and show the influence of mutual intelligibility when the models are applied under zero-shot settings. We will publicly release our models for research.

Visit

arxiv.org

Tasks

language modeling

Tags

serengeti

Similaires

On the Calibration of Massively Multilingual Language ModelsGlotEval: A Test Suite for Massively Multilingual Evaluation of Large Language ModelsEMMA-500: Enhancing Massively Multilingual Adaptation of Large Language ModelsMassively Multilingual Adaptation of Large Language Models Using Bilingual Translation DataMassively Multilingual Transfer for NERFleurs-SLU: A Massively Multilingual Benchmark for Spoken Language Understanding

On the Calibration of Massively Multilingual Language Models

Massively Multilingual Language Models (MMLMs) have recently gained popularity due to their surprisi

GlotEval: A Test Suite for Massively Multilingual Evaluation of Large Language Models

Large language models (LLMs) are advancing at an unprecedented pace globally, with regions increasin

EMMA-500: Enhancing Massively Multilingual Adaptation of Large Language Models

In this work, we introduce EMMA-500, a large-scale multilingual language model continue-trained on t

Massively Multilingual Adaptation of Large Language Models Using Bilingual Translation Data

This paper investigates a critical design decision in the practice of massively multilingual continu

Massively Multilingual Transfer for NER

In cross-lingual transfer, NLP models over one or more source languages are applied to a low-resource target language. While most prior work has used a single source model or a few carefully selected models, here we consider a `massive' setting with many such model

Fleurs-SLU: A Massively Multilingual Benchmark for Spoken Language Understanding

Spoken language understanding (SLU) is indispensable for half of all living languages that lack a fo