Domain-Specific Large Language Model for Renewable Energy and Hydrogen Deployment Strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent advances in large language models (LLMs) have shown promise in specialized fields, yet their effectiveness is often constrained by limited domain expertise. We present a renewable and hydrogen energy-focused LLM developed by fine-tuning LLaMA 3.1 8B on a curated renewable energy corpus (RE-LLaMA). Through continued pretraining on domain-specific data, we enhanced the model’s capabilities in renewable energy contexts. Extensive evaluation using zero-shot and few-shot prompting demonstrated that our fine-tuned model significantly outperformed the base model across renewable and hydrogen energy tasks. This work establishes the viability of specialized, smaller-scale LLMs and provides a framework for developing domain-specific models that can support advanced research and decision-making in the renewable energy sector. Our approach represents a significant step forward in applying LLMs to the renewable and hydrogen energy sector, offering potential applications in advanced research and decision-making processes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it