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Thomas A. Ruggles

Government of the United States of America - Oak Ridge National Laboratory

TN

United States

  • Government of the United States of America - Oak Ridge National Laboratory

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Scholarly Papers (1)

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From Regulatory Text to Structured Data: A Domain-Specific LLM Framework for Extracting Hydropower Operational Constraints

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From Regulatory Text to Structured Data: A Domain-Specific LLM Framework for Extracting Hydropower Operational Constraints

Number of pages: 23 Posted: 09 Apr 2026
Faisal Bin Ashraf, Hong-Jun Yoon, Carly H. Hansen, Thomas A. Ruggles, Debjani Singh, Esther S. Parish and Shih-Chieh Kao
University of Oulu, Government of the United States of America - Oak Ridge National Laboratory, affiliation not provided to SSRN, Government of the United States of America - Oak Ridge National Laboratory, Government of the United States of America - Oak Ridge National Laboratory, Government of the United States of America - Oak Ridge National Laboratory and Government of the United States of America - Oak Ridge National Laboratory
Downloads 17 (1,548,841)

Abstract:

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Large language models, information extraction, Prompt Engineering, regulatory documents, Natural Language Processing, domain knowledge

From Regulatory Text to Structured Data: A Domain-Specific LLM Framework for Extracting Hydropower Operational Constraints

Number of pages: 23 Posted: 16 Jun 2026
Faisal Bin Ashraf, Hong-Jun Yoon, Carly H. Hansen, Thomas A. Ruggles, Debjani Singh, Esther S. Parish and Shih-Chieh Kao
University of Oulu, Government of the United States of America - Oak Ridge National Laboratory, affiliation not provided to SSRN, Government of the United States of America - Oak Ridge National Laboratory, Government of the United States of America - Oak Ridge National Laboratory, Government of the United States of America - Oak Ridge National Laboratory and Government of the United States of America - Oak Ridge National Laboratory
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Abstract:

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large language models, information extraction, Prompt Engineering, regulatory documents, Natural Language Processing, domain knowledge