DeEscalWild: A Real-World Benchmark for Automated De-Escalation Training with SLMs
Effective de-escalation is critical for law enforcement safety and community trust, yet traditional training methods lack scalability and realism. While Large Language Models (LLMs) enable dynamic, open-ended simulations, their substantial computational footprint renders them impractical for deployment on the lightweight, portable hardware required for immersive field training. Small Language Models (SLMs) offer a viable real-time alternative but suffer from a critical scarcity of high-quality, domain-specific training data. To bridge this gap, we present DeEscalWild, a novel benchmark dataset curated from a multi-stage pipeline of in-the-wild police-civilian interactions extracted from p...
| Authors | Md Hasebul Hasan, Krity Haque Charu, Eshwara Prasad Sridhar et al. |
|---|---|
| Primary category | cs.CL |
| Published | 20 March 2026 |
| Last updated at source | 7 May 2026 |
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