从脚本到语义：非洲自然语言推理的提示策略
From Script to Semantics: Prompting Strategies for African NLI
AI总结：
本研究系统评估了五种提示策略在斯瓦希里语、约鲁巴语和豪萨语的自然语言推理任务上的表现，发现对比提示策略最可靠且能显著提升模型性能。
摘要：
大型语言模型（LLMs）在多语言环境中的评估日益增多，但它们在低资源非洲语言中的推理行为仍未得到充分探索，尤其是在无微调的纯提示设置下。我们使用AfriXNLI基准，对斯瓦希里语、约鲁巴语和豪萨语的自然语言推理（NLI）提示策略进行了系统研究。我们评估了五种提示策略：基线（零样本）、脚本感知、语言特定、对比和原生标签自翻译（NL-STP），使用了两个中等规模的开源模型（Llama3.2-3B和Gemma3-4B）。为隔离提示设计的影响，我们的研究中排除了少样本示例和思维链推理的影响。我们发现不同策略在类别性能上存在显著差异，某些配置中出现高度中性类崩溃和高预测偏斜。对比提示被证明是最可靠的策略，在不同语言和模型上持续改进，并具有更好的类别行为平衡和整体准确率提升。值得注意的是，精心构建的提示足以击败提供少样本提示和思维链提示的更强大基线。我们发现提示表述对于低资源语言的多语言NLI至关重要，并且语言感知的决策结构可以有效地增强资源受限环境下的鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
