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        <title>From Script to Semantics: Prompting Strategies for African NLI</title>
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            <namePart type="family">Matfunjwa</namePart>
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            <namePart type="given">Mmasibidi</namePart>
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    <abstract>Large language models (LLMs) are increasingly evaluated in multilingual settings, yet their inference behavior in low-resource African languages remains underexplored especially under pure prompting without fine-tuning. We present a systematic study of prompting strategies for Natural Language Inference (NLI) in Swahili, Yoruba, and Hausa using the AfriXNLI benchmark. We evaluate five prompting strategies Baseline (zero-shot), Script-Aware, Language Specific, Contrastive, and Native-Label Self-Translation (NL-STP) across two mid-sized open weight models (Llama3.2-3B and Gemma3-4B). To isolate the effect of prompt design, the effect of few-shot examples and Chain-of-Thought reasoning is eliminated in our study. We find a significant difference in performance of class wise across strategies with highly neutral class collapse and high prediction skew in some configurations. Contrastive prompting proves to be the most reliable and steadily improving strategy over language and model and has better balance of class behavior and balance of overall accuracy gains. Notably, well-constructed prompts are sufficient to beat more powerful baselines that are provided with few-shot prompts and Chain-of-Thought prompts. We have found that prompt formulation is essential to multilingual NLI with low-resource languages and that language aware decision structuring can be used to meaningfully enhance robustness in resource challenged settings.</abstract>
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%0 Conference Proceedings
%T From Script to Semantics: Prompting Strategies for African NLI
%A Tiwari, Anuj
%A Oko-odion, Terry
%A Nwokocha, Hannah
%Y Matfunjwa, Muzi
%Y Setaka, Mmasibidi
%Y Mabuya, Rooweither
%Y van Zaanen, Menno
%S Proceedings of Resources for African Indigenous Languages (RAIL) 2026 @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F tiwari-etal-2026-script
%X Large language models (LLMs) are increasingly evaluated in multilingual settings, yet their inference behavior in low-resource African languages remains underexplored especially under pure prompting without fine-tuning. We present a systematic study of prompting strategies for Natural Language Inference (NLI) in Swahili, Yoruba, and Hausa using the AfriXNLI benchmark. We evaluate five prompting strategies Baseline (zero-shot), Script-Aware, Language Specific, Contrastive, and Native-Label Self-Translation (NL-STP) across two mid-sized open weight models (Llama3.2-3B and Gemma3-4B). To isolate the effect of prompt design, the effect of few-shot examples and Chain-of-Thought reasoning is eliminated in our study. We find a significant difference in performance of class wise across strategies with highly neutral class collapse and high prediction skew in some configurations. Contrastive prompting proves to be the most reliable and steadily improving strategy over language and model and has better balance of class behavior and balance of overall accuracy gains. Notably, well-constructed prompts are sufficient to beat more powerful baselines that are provided with few-shot prompts and Chain-of-Thought prompts. We have found that prompt formulation is essential to multilingual NLI with low-resource languages and that language aware decision structuring can be used to meaningfully enhance robustness in resource challenged settings.
%R 10.63317/3eydkjk4pzb7
%U https://aclanthology.org/2026.rail-1.8/
%U https://doi.org/10.63317/3eydkjk4pzb7
%P 72-83

Markdown (Informal)

[From Script to Semantics: Prompting Strategies for African NLI](https://aclanthology.org/2026.rail-1.8/) (Tiwari et al., RAIL 2026)

• From Script to Semantics: Prompting Strategies for African NLI (Tiwari et al., RAIL 2026)

ACL

• Anuj Tiwari, Terry Oko-odion, and Hannah Nwokocha. 2026. From Script to Semantics: Prompting Strategies for African NLI. In Proceedings of Resources for African Indigenous Languages (RAIL) 2026 @ LREC 2026, pages 72–83, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).