Scaling Open-Weight Large Language Models for Hydropower Regulatory Information Extraction: A Systematic Analysis

• URL: http://arxiv.org/abs/2511.11821v1
• Date: Fri, 14 Nov 2025 19:23:25 GMT
• Title: Scaling Open-Weight Large Language Models for Hydropower Regulatory Information Extraction: A Systematic Analysis
• Authors: Hong-Jun Yoon, Faisal Ashraf, Thomas A. Ruggles, Debjani Singh,
• Abstract summary: Information extraction from regulatory documents using large language models presents critical trade-offs between performance and computational resources.<n>We evaluated seven open-weight models (0.6B-70B parameters) on hydropower licensing documentation to provide empirical deployment guidance.
• Score: 0.2555802789878571
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Information extraction from regulatory documents using large language models presents critical trade-offs between performance and computational resources. We evaluated seven open-weight models (0.6B-70B parameters) on hydropower licensing documentation to provide empirical deployment guidance. Our analysis identified a pronounced 14B parameter threshold where validation methods transition from ineffective (F1 $<$ 0.15) to viable (F1 = 0.64). Consumer-deployable models achieve 64\% F1 through appropriate validation, while smaller models plateau at 51\%. Large-scale models approach 77\% F1 but require enterprise infrastructure. We identified systematic hallucination patterns where perfect recall indicates extraction failure rather than success in smaller models. Our findings establish the first comprehensive resource-performance mapping for open-weight information extraction in regulatory contexts, enabling evidence-based model selection. These results provide immediate value for hydropower compliance while contributing insights into parameter scaling effects that generalize across information extraction tasks.

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