Fostering Digital Inclusion for Low-Resource N igerian Languages: A Case Study of I gbo and N igerian P idgin (2025)

Abstract: Current state-of-the-art large language models (LLMs) like GPT-4 perform exceptionally well in language translation tasks for high-resource languages, such as English, but often lack high accuracy results for low-resource African languages such as Igbo and Nigerian Pidgin, two native languages in Nigeria. This study addresses the need for Artificial Intelligence (AI) linguistic diversity by creating benchmark datasets for Igbo-English and Nigerian Pidgin-English language translation tasks. The dataset developed is curated from reputable online sources and meticulously annotated by crowd-sourced native-speaking human annotators. Using the datasets, we evaluate the translation abilities of GPT-based models alongside other state-of-the-art translation models specifically designed for low-resource languages. Our results demonstrate that current state-of-the-art models outperform GPT-based models in translation tasks. In addition, these datasets can significantly enhance LLM performance in these translation tasks, marking a step toward reducing linguistic bias and promoting more inclusive AI models.

## introduction machine translation (mt) is a fundamental subfield of natural language processing (nlp) that involves translating text or speech between different languages using computational systems. mt has been around for a while, and its two classic methods are traditional rule-based systems and statistical machine translation @xcite . recently, neural machine translation (nmt) has revolutionized language translation as it uses deep learning techniques to model the translation process @xcite . this approach demonstrates promising results in bilingual and multilingual machine translations @xcite . the term large language models (llms) is used to describe models that are built on so-phisticated deep learning architectures, typically transformers, and are characterized by billions of parameters and extensive training data. these models can achieve high accuracy in various tasks -including machine translation -which is a great leap in developments in the field of natural language processing @xcite . while machine translation continues to advance, the vast majority of improvements made in the last decade have been for high-resource languages, or languages that have large quantities of training data available digitally. high-resource languages such as english, german and spanish are available in abundant resources such as books, movies, and digital texts @xcite . in contrast, low-resource languages (lrls) are those that are less studied, resource-scarce, or less digitized, among other denominations @xcite . as of today, most nlp research focuses on a select few languages out of the vast amounts of languages spoken around the world, leaving majority of languages-particularly lowresource african languages largely understudied. one of the major challenges to machine translation is the ability to obtain high-quality monolingual and parallel-sentence pair datasets. this ensures that the results are generated with high precision and accuracy. unfortunately, this has been a major challenge to african language translation. this problem highlights the need to increase the linguistic diversity of african languages in the nlp fields. to address this issue, we introduce high-quality benchmark datasets in lowresource nigerian languages -igbo and nigerian pidgin -developed through a thorough process of collection, pre-processing, and annotation. in mt tasks, the parallel corpus, consists of sentences in both the source and target languages @xcite . in our case, the source languages are igbo and nigerian pidgin, with english as the target language. this paper presents two main contribu-tions: first, it establishes benchmark datasets for igbo-english and nigerian pidgin-english machine translation tasks. we curated a combined total of over 2 million monolingual sentences for both igbo and nigerian pidgin from online sources. using this dataset, we generated parallel datasets through a combination of crowd-sourced human annotations (amazon mechanical turk) and outputs from large language models (gpt-3.5 and 4). second, it leverages the igbo-english benchmark datasetalong with other open-source datasets in similar language pairs -to provide a comparative analysis of the translation ability of current large language models (llms) to select state of the art machine translation models that have been shown to perform well for low-resource languages. the results demonstrate that the fine-tuned models outperform both their original version and the gpt-4 models in the igbo-english translation task, highlighting the impact of the datasets and the effectiveness of fine-tuning on llm performance. ## related work recent research efforts have focused on addressing the scarcity of resources for low-resource languages by creating specialized datasets and applying them to fine-tune llms for translation tasks. ## data acquisition and preparation in this section, we present an overview of our data preparation and acquisition process, followed by a detailed explanation of our data annotation approach. we employ the use of llms alongside crowdsourced native speakers as annotators to curate and annotate the dataset, ultimately creating a parallel dataset that maps the source languages (igbo or nigerian pidgin) to the target language (english). foot_0 ## experiments in this section, we present a benchmark use case for our curated parallel datasets alongside existing datasets. using these datasets, we evaluate the translation quality of state-of-the-art multilingual translation models optimized for low-resource languages to that of large language models. next, we present an overview of the open-source dataset utilized in addition to our curated dataset for the comparative analysis. ## conclusion and future work in this paper, we present benchmark datasets for two low-resource nigerian languages: igbo and nigerian pidgin. we demonstrate the use case of the dataset by leveraging the igbo benchmark datasets, combined with other open-source benchmark datasets, to significantly improve state-ofthe-art translation models for low-resource languages. our result significantly boosts the models' performance in low-resource language translation tasks, with bleu scores exceeding those of the baseline models, including gpt-4o. this result demonstrates the effectiveness of fine-tuning for low-resource languages. beyond the technical contribution, our research plays an important role in the diversification of ai datasets by promoting linguistic inclusivity. the establishment of robust benchmark datasets for igbo and nigerian pidgin provides a critical foundation for future research and development, helping reduce linguistic bias and expanding the scope of machine translation to underrepresented languages. this work not only underscores the potential of llms in low-resource settings but also opens new avenues for creating more equitable ai applications across languages.