Abstract
Given the complexity of fiscal data types and the lengthy policy execution chain, this study constructs an application framework for language models supporting government decision-making. It systematically investigates task modules including decision-making question-answering identification, expenditure forecasting modeling, executive summary extraction, semantic matching, and conflict reasoning. The framework elucidates model architecture design methodologies and semantic fusion mechanisms, while introducing response capability simulation testing and performance evaluation systems. Using heterogeneous fiscal corpora and multi-task experimental data, demonstrates that the model exhibits strong performance in accuracy, generative consistency, and generalization capabilities, supporting intelligent applications across diverse fiscal scenarios.
Keywords
Language ModelSemantic MatchingExecution Analysis
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Cite This Article
Ren, L. (2025). Ai-powered financial insights: using large language models to improve government decision-making and policy execution. Journal of Industrial Engineering and Applied Science, 3(5), 21-26. https://doi.org/10.70393/6a69656173.333139
Acknowledgments
The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.
Funding
Not applicable.
Institutional Review Board Statement
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Data Availability Statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Informed Consent Statement
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Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Author Contributions
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References
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Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.
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