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

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

Artificial Intelligence (cs.AI)

arXiv:2609.27197 [cs.AI]

(or arXiv:2609.27197v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.27197 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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