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This study investigates the effectiveness of Large Language Models as decision-support agents in wildfire risk assessment using the Analytic Hierarchy Process.

March 28, 2026FireOpen Access

Retrieval over Response: Large Language Model-Augmented Decision Strategies for Hierarchical Wildfire Risk Evaluation

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This study investigates the effectiveness of Large Language Models as decision-support agents in wildfire risk assessment using the Analytic Hierarchy Process.
Analyzed four LLM-assisted decision-making strategies: Direct LLM Scoring, Multi-Model Debate Scoring, Full-Document Prompting, and Indicator-Guided Prompting.
Evaluated LLM-generated rankings against expert-defined ground truth across 16 sub-criteria.
Used the mean correlation coefficient R to assess effectiveness of the strategies.
Direct LLM Scoring showed minimal correlation with expert rankings (R = 0.009 ± 0.070).
Multi-Model Debate Scoring demonstrated slight improvements (R = 0.181).
Full-Document Prompting was unstable with scores (R = 0.081 ± 0.189).
Indicator-Guided Prompting exhibited the highest agreement with expert rankings (R = 0.598 ± 0.065), suggesting effectiveness of structured contextual inputs.

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A study examines LLM-assisted decision strategies for wildfire risk evaluation, suggesting improved decision-making methods.

Cite This Study

Cheng et al. (2026) studied this question.

synapsesocial.com/papers/69c771dd8bbfbc51511e1ee9 https://doi.org/10.3390/fire9040143

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Retrieval over Response: Large Language Model-Augmented Decision Strategies for Hierarchical Wildfire Risk Evaluation

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Cite This Study

Retrieval over Response: Large Language Model-Augmented Decision Strategies for Hierarchical Wildfire Risk Evaluation

Key Points

Discussion

Implication

Cite This Study

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