Enhancing Small Language Models for Power Outage Report Generation via Minimum Risk Training

Applying Minimum Risk Training to power outage report generation raised Qwen2.5-7B-Instruct's overall accuracy from 16.20% to 68.95%, according to an arXiv paper (2609.27197v1) on the Outage Data Initiative Nationwide (ODIN). The approach optimizes sequence-level evaluation metrics rather than token-level maximum-likelihood objectives, transforming heterogeneous reports into standardized XML compliant with CIM IEC 61968-3. The authors present the result as evidence that sequence-level optimization is effective for domain-specific structured generation.

arXiv:2609.27197v1 Announce Type: new Abstract: Minimum Risk Training MRT enables neural machine translation models to directly optimize sequence-level evaluation metrics instead of relying only on token- level maximum-likelihood objectives Shen et al. 2016 . Although introduced a decade ago, recent work shows renewed potential for risk-based optimization in modern language models Yang et al. 2024 , Jinnai et al. 2025 . We apply MRT to power outage report generation for the Outage Data Initiative Nationwide ODIN , transforming heterogeneous reports into standardized XML compliant with CIM IEC 61968-3. Our MRT approach improves Qwen2.5-7B-Instruct overall accuracy from 16.20% to 68.95%, demonstrating the effectiveness of sequence- level optimization for domain-specific structured generation