这篇论文发现AI在尼日利亚话语中常误解真实意图，他们设计的MIF框架让Gemini 2.5 Flash的注册识别准确率从33%跳到73%，成果很实在。
论文提出九维意义智能框架（MIF），用于尼日利亚公共话语的上下文感知评估。现有基准NaijaSenti和AfriSenti仅做三向情感分类。MIF在30项校准数据集上评估Gemini 2.5 Flash，零样本下注册分类准确率33.3%，使用MIF后升至73.3%。复合意义智能分数从73.2升至78.6。编码潜台词检测提升10点，战略行动推荐提升10.3点。框架、指南和校准集已开源。
The Register Gap: A Meaning Intelligence Framework for Nigerian Public Discourse
We introduce the Meaning Intelligence Framework (MIF), a nine-dimension annotation and evaluation schema for Nigerian public discourse that separates surface sentiment from true communicative intent. Existing benchmarks for Nigerian languages, including NaijaSenti and AfriSenti, treat sentiment classification as a three-way polarity task (positive, negative, neutral). We argue that the dominant failure mode of AI systems on Nigerian discourse is not translation failure but context failure: the same utterance carries opposite pragmatic force depending on speaker, audience, and situation. The MIF operationalises this insight across nine scored dimensions: register, surface sentiment, true intent, irony, coded subtext, risk tier, annotator confidence, speaker emotion, and recommended communications action. We construct a 30-item calibration dataset spanning Standard English, Nigerian English, Nigerian Pidgin, and code-mixed registers, and evaluate a frontier language model (Gemini 2.5 Flash) under zero-shot and schema-informed prompting conditions. The headline finding is the Register Gap: zero-shot register classification accuracy is 33.3%, rising to 73.3% (+40 points) when the model receives the MIF schema in-context. The composite Meaning Intelligence Score increases by 5.4 points (73.2 to 78.6) under schema-informed prompting, with the largest practical gains in register identification, coded-subtext detection (+10 points), and strategic action recommendation (+10.3 points). We release the framework specification, annotation guidelines, and the 30-item public calibration set to support reproducibility, while retaining a private holdout corpus for contamination-protected evaluation.