Freedisch/yeye

The paper demonstrates how LLMs can be used to support emergency report by using local infrastructure data: study case on TOGO # yeye **Adapting Large Language Models for Emergency Dispatch in Togo Using Local Infrastructure Data** yeye is a framework for constructing geospatially grounded instruction-tuning datasets from real infrastructure data and using them to fine-tune LLMs for emergency dispatch decision support in low-resource settings. Submitted to **Deep Learning Indaba 2026** — AI for Social Impact and Sustainable Systems track. > ⚠️ **Research prototype only.** yeye must not be deployed in any operational emergency dispatch context. See Limitations. --- ## Overview Understaffed emergency call centers in countries like Togo cannot guarantee a trained human operator for every incoming call. Off-the-shelf LLMs like Mistral-7B have no knowledge of Togo's health facilities, road networks, or resource constraints — this information is too sparsely documented online for models to acquire from pretraining alone. yeye addresses this by: 1. Extracting infrastructure data from **OpenStreetMap** and **UN OCHA Humanitarian Data Exchange** 2. Programmatically generating **1,000 instruction-tuning pairs** spanning 12 emergency types across 15 Togo cities 3. Fine-tuning **Mistral-7B** via **LoRA** (QLoRA 4-bit) on a single T4 GPU 4. Evaluating on a **blind test set** where all infrastructure context is withheld from prompts ## Results | Metric | Base Mistral-7B | yeye | Delta | | --------------------- | --------------- | ------ | --------------- | | Combined score | 73.8% | 79.6% | +5.8pp | | Facility grounding | 67.5% | 88.5% | +21.0pp | | Exact facility recall | 38.0% | 87.0% | **+49.0pp** | | Win rate | 34/100 | 60/100 | McNemar p=0.007 | The headline result: exact facility name recall improves from 38% to 87% under blind evaluation — a 49 percentage-point gain (95% CI: +37.4 to +60.6 percentage points). ## Dataset The infrastructure dataset contains **1,903 facili …