From Script to Semantics: Prompting Strategies for African NLI
2026-06-02 • Computation and Language
Computation and LanguageMachine Learning
AI summaryⓘ
The authors studied how large language models understand the meaning of sentences in three African languages with limited resources. They tested different ways of asking the models questions (called prompting strategies) to see which worked best without extra training. They found that the 'contrastive prompting' method gave the most balanced and reliable results across models and languages. Their work shows that carefully designing prompts can improve language understanding in low-resource multilingual settings without needing complex setups.
large language modelsnatural language inferenceprompting strategieslow-resource languagesAfrican languagesSwahiliYorubaHausacontrastive promptingmultilingual NLP
Authors
Anuj Tiwari, Terry Oko-odion, Hannah Nwokocha
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.