Mining Large Language Models for Low-Resource Language Data: Comparing Elicitation Strategies for Hausa and Fongbe

Abstract

Large language models (LLMs) are trained on data contributed by low-resource language communities, including curated datasets such as MasakhaNER and MAFAND-MT, yet the linguistic knowledge encoded in these models remains accessible only through commercial APIs. This paper investigates whether strategic prompting can extract usable text data from LLMs for two West African languages: Hausa (Afroasiatic, approximately 80 million speakers) and Fongbe (Niger-Congo, approximately 2 million speakers). We systematically compare six elicitation task types: creative writing, functional text, structured knowledge, dialogue, topic-switching probes, and constrained generation across two commercial LLMs (GPT-4o Mini and Gemini 2.5 Flash). Generated outputs are evaluated on linguistic accuracy, lexical diversity, domain coverage, and code-switching rates through automatic metrics assessment. Our findings reveal that elicitation strategy significantly affects output quality and that optimal strategies differ by language: Hausa benefits from volume-maximizing tasks such as functional text and dialogue, while Fongbe requires constraint-heavy prompts that enforce monolingual output. GPT-4o Mini extracts 6–41x more usable target-language words per API call than Gemini, though Gemini achieves higher language purity for Fongbe on constrained tasks. We provide a practical framework for low-resource language communities to maximize usable data extraction from LLMs and release all generated corpora and code.

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