This study presents the first application of a Retrieval Augmented Generation (RAG) model for Nepali legal question answering, addressing data scarcity in low-resource languages. Using BM25 on chunked documents, the RAG pipeline achieved high precision and truthfulness, demonstrating its effectiveness in the Nepali legal domain.
Nsanku is a systematic benchmark evaluating the zero-shot machine translation performance of 19 LLMs across 43 Ghanaian languages. It utilizes Bible sentences and metrics like BLEU and chrF, with gemini-2.5-flash achieving the highest overall average score.
This paper introduces a novel in-context learning approach for low-resource Coptic to English machine translation, augmenting inputs with syntactic information from Universal Dependencies parses. Combining this syntactic data with dictionary-based glosses achieves significant gains and sets a new state-of-the-art.
Este artigo propõe um arcabouço teórico para estudar a transferência interlinguística e a adaptação eficiente de parâmetros em LLMs multilingues para a família de línguas túrquicas. Ele busca abordar a sub-representação de línguas de baixos recursos nesses modelos, como azerbaijano, cazaque, uzbeque, turcomeno e gagauz.
This paper introduces AFRILANGDICT, a collection of African language-English dictionary entries, and AFRILANGEDU, a dataset. These resources are used to train AI models, called AFRILANGTUTOR, for language tutoring in low-resource African languages, addressing the scarcity of AI systems for local languages on the African continent.
MultiSoc-4D is a new Bengali social media dataset benchmark designed to diagnose LLM behavior in closed-set annotation. The research identifies "instruction-induced label collapse," a phenomenon where LLMs systematically prefer fallback labels, leading to under-detection of minority categories.
Low-resource natural language processing has experienced explosive growth, but its evaluation faces a critical challenge: the scarcity of sociolinguistic expertise needed to assess complex generative systems. This creates an "Annotation Scarcity Paradox," where the technical capacity to scale models vastly outpaces the human infrastructure required for authentic evaluation.
This research addresses the Stability-Expressivity Gap in Spoken Language Models (SLMs) for low-resource languages, caused by the extensive use of synthetic data. While synthetic data improves phonetic accuracy, it degrades prosodic expressivity, a phenomenon termed Synthetic Erosion. The paper introduces self-alignment frameworks to recover expressivity.
Este estudo empírico investiga o aprendizado em contexto (ICL) de muitos exemplos para tradução automática de inglês para dez idiomas de baixo recurso. Os achados mostram que o ICL se torna mais eficaz com o aumento do número de exemplos, e a recuperação baseada em BM25 melhora substancialmente a eficiência dos dados.