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Source note2 min read

POTracker: Optimizing Large Language Models for Standard-Compliant Power Outage Report Generation

Source

arXiv

arxiv.org/abs/2606.23533v1

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Plain-Language Brief

Researchers have developed POTracker, an optimized large language model (LLM) for generating power outage reports that follow strict formatting and structural rules required by energy-sector regulatory bodies in the United States. The new loss function, POTrackerLoss, considers both textual similarity and structural similarity between the generated report and the ground-truth report. Results show that POTracker outperforms other fine-tuning approaches in accuracy for power outage reports.

What the Document Says

  • The study presents POTracker, an optimized LLM for generating power outage reports complying with strict formatting and structural rules.
  • POTracker is fine-tuned using Qwen2.5-7B-Instruct with a new loss function called POTrackerLoss.
  • POTracker considers both textual similarity and structural (tag) similarity between the generated report and the ground-truth report.
  • Results show that POTracker outperforms other fine-tuning approaches in accuracy for power outage reports, improving overall accuracy by up to 51% and reaching 86.47% structural accuracy.

Key Concepts Mentioned

  • Large language models
  • Power outage reports
  • Formatting rules
  • Structural rules
  • Energy-sector regulatory bodies
  • Fine-tuning
  • Loss function
  • Performance evaluation

Claims Made by the Document

  • POTracker is an optimized LLM for power outage report generation.
  • POTracker outperforms other fine-tuning approaches in accuracy for power outage reports.

Evidence Used

  • The text describes the development and performance of POTracker.
  • Results are provided showing the improvement in accuracy compared to other fine-tuning methods.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Recent large language models (LLMs) are good at general text generation, but it is still hard to use them for domain-specific data generation because the output must follow strict formatting and structural rules. Unlike open-ended tasks such as question answering or translation, domain-specific generation must be both semantically correct and compliant with existing guidelines and standards. In this work, we study the nationwide interoperability problem of utility power outage reports in the United States. In practice, outage reports need to be machine-readable (e.g., JSON or XML) and must strictly follow requirements from energy-sector regulatory bodies. To address this problem, we propose POTracker, an optimized LLM for power outage report generation. We fine-tune Qwen2.5-7B-Instruct using our proposed objective. The key contribution is a new loss function, POTrackerLoss, that considers both textual similarity and structural (tag) similarity between the generated report and the ground-truth report. We evaluate POTracker on a dataset of 1,000 power outage reports and compare it with five well-known fine-tuning methods and one rule-based XML conversion method. Results show that POTracker outperforms other fine-tuning approaches, improving overall accuracy by up to 51% and reaching 86.47% structural accuracy for generated power outage reports. In addition, we conduct a human study to assess the quality of the ground-truth standard reports, where domain experts assign the generated labels an average score of 4.03 on a 0--5 scale.