arXiv:2601.09716v2 Announce Type: replace Abstract: Natural Language Processing (NLP) is rapidly transforming research methodologies across disciplines, yet African languages remain largely underrepr...
arXiv:2610.28173v1 Announce Type: cross Abstract: Large Language Models (LLMs) depend fundamentally on the diversity, depth, and factual rigor of their pre-training corpora. However, contemporary fou...
arXiv:2606. 12708v1 Announce Type: cross Abstract: Despite their linguistic diversity and global significance, African languages remain underrepresented in research and resources to support NLP.
The paper surveys NLP research on Nigeria’s three major low‑resource languages—Hausa, Yoruba, and Igbo—covering over 500 languages spoken by 175 million people. It reviews 293 studies, finding that only 27.6% produced new linguistic resources, indicating a heavy reliance on repurposing existing data. The authors highlight under‑explored challenges such as morphological analysis and diacritic representation, and call for collaborative resource enrichment and community support to advance NaijaNLP and low‑resource NLP more broadly.
The paper audits 51 recent multilingual evaluation benchmarks covering 242 datasets and 219 languages, focusing on coverage, representativeness, and rigor. It finds that while coverage is broad, many languages appear in only one benchmark and entire regions are underrepresented; low‑resource languages face a task equity gap. Representativeness is compromised by heavy reliance on English translations, and rigor is weakened by benchmark contamination and parallel‑corpus overlap. The authors propose concrete recommendations for building evaluation frameworks that are natively constructed, culturally grounded, contamination‑aware, and community‑aligned.
The paper presents the first systematic bibliometric survey of Tibetan NLP, covering 140 publications from 2004 to 2026. It shows rapid growth—84% of the work appears since 2018—and a bottom‑up expansion across the NLP pipeline, from corpora and word segmentation to large language models. The study highlights that most research originates in China, identifies gaps in dialect coverage, responsible NLP, and resource release, and calls for open science and community participation to support Tibetan and other low‑resource languages.
The paper audits 51 multilingual evaluation benchmarks covering 242 datasets and 219 languages, focusing on coverage, representativeness, and rigor. It finds that while coverage is broad, many languages appear in only one benchmark and entire regions are underrepresented, with low-resource languages evaluated on far fewer tasks than high-resource ones. The study also highlights that most datasets rely on English translations, creating artifacts and limiting cultural relevance, and that benchmark contamination through translation leakage and corpus overlap undermines rigor. Recommendations call for natively constructed, culturally grounded, contamination-aware evaluation frameworks that truly serve the communities they aim to assess.
arXiv:2608.30107v1 Announce Type: cross Abstract: Understanding which countries are represented in NLP datasets is essential for identifying gaps, targeting data collection, measuring progress, and i...
arXiv:2608.21369v1 Announce Type: cross Abstract: Nigerian Pidgin is one of Africa's most widely spoken languages, yet remains severely underrepresented in language model evaluation. Existing benchma...
arXiv:2607. 20241v1 Announce Type: cross Abstract: Culturally loaded translation poses unique challenges for machine translation (MT), as meanings are deeply embedded in socio-cultural contexts beyond surface linguistic forms.
AfrIFact is a new dataset designed to support automatic fact‑checking in ten African languages and English, covering information retrieval, evidence extraction, and fact verification. Experiments show that current embedding models struggle with cross‑lingual retrieval, and that cultural and news documents are easier to retrieve than healthcare documents. Large language models also lack robust multilingual fact‑verification, but few‑shot prompting and task‑specific fine‑tuning can significantly improve performance.
arXiv:2609.22494v1 Announce Type: new Abstract: In recent years, there has been a surge of interest in Cultural NLP, with substantial efforts to create globally inclusive NLP systems. The rapid growt...