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Towards EnergyGPT: A Large Language Model Specialized for the Energy Sector

Amal Chebbi, Babajide Kolade

TL;DR

EnergyGPT presents a domain-adapted large language model for the energy sector by fine-tuning LLaMA 3.1-8B on a curated energy corpus. The study contrasts full-parameter supervised fine-tuning (SFT) with a parameter-efficient LoRA approach, detailing a complete data pipeline (collection, cleaning, deduplication, semantic filtering, balancing) and a pairing strategy to create contextually coherent training signals. A bespoke 476-question benchmark, augmented with calibrated LLM judges (Claude-Sonnet-4 and GPT-4.1-mini) and human raters, demonstrates energy-domain gains over the base model, with SFT delivering larger factual/technical improvements and LoRA offering competitive gains at reduced cost. The work further demonstrates production deployment on-premises (NIMs) and in Azure, discusses generalizability to other domains, and outlines limitations such as lack of retrieval augmentation and explicit physical reasoning, proposing a roadmap toward richer reasoning and grounding. Overall, EnergyGPT offers a practical, scalable recipe for adapting foundation models to specialized technical domains and provides a transparent framework for reproducibility and deployment in real-world settings.

Abstract

Large language models have demonstrated impressive capabilities across various domains. However, their general-purpose nature often limits their effectiveness in specialized fields such as energy, where deep technical expertise and precise domain knowledge are essential. In this paper, we introduce EnergyGPT, a domain-specialized language model tailored for the energy sector, developed by fine-tuning the LLaMA 3.1-8B model on a high-quality, curated corpus of energy-related texts. We consider two adaptation strategies: a full-parameter Supervised Fine-Tuning variant and a parameter-efficient LoRA-based variant that updates only a small fraction of the model parameters. We present a complete development pipeline, including data collection and curation, model fine-tuning, benchmark design and LLM-judge choice, evaluation, and deployment. Through this work, we demonstrate that our training strategy enables improvements in domain relevance and performance without the need for large-scale infrastructure. By evaluating the performance of both EnergyGPT variants using domain-specific question-answering benchmarks, our results show that the adapted models consistently outperform the base model in most energy-related language understanding and generation tasks, with the LoRA variant achieving competitive gains at significantly reduced training cost.

Towards EnergyGPT: A Large Language Model Specialized for the Energy Sector

TL;DR

EnergyGPT presents a domain-adapted large language model for the energy sector by fine-tuning LLaMA 3.1-8B on a curated energy corpus. The study contrasts full-parameter supervised fine-tuning (SFT) with a parameter-efficient LoRA approach, detailing a complete data pipeline (collection, cleaning, deduplication, semantic filtering, balancing) and a pairing strategy to create contextually coherent training signals. A bespoke 476-question benchmark, augmented with calibrated LLM judges (Claude-Sonnet-4 and GPT-4.1-mini) and human raters, demonstrates energy-domain gains over the base model, with SFT delivering larger factual/technical improvements and LoRA offering competitive gains at reduced cost. The work further demonstrates production deployment on-premises (NIMs) and in Azure, discusses generalizability to other domains, and outlines limitations such as lack of retrieval augmentation and explicit physical reasoning, proposing a roadmap toward richer reasoning and grounding. Overall, EnergyGPT offers a practical, scalable recipe for adapting foundation models to specialized technical domains and provides a transparent framework for reproducibility and deployment in real-world settings.

Abstract

Large language models have demonstrated impressive capabilities across various domains. However, their general-purpose nature often limits their effectiveness in specialized fields such as energy, where deep technical expertise and precise domain knowledge are essential. In this paper, we introduce EnergyGPT, a domain-specialized language model tailored for the energy sector, developed by fine-tuning the LLaMA 3.1-8B model on a high-quality, curated corpus of energy-related texts. We consider two adaptation strategies: a full-parameter Supervised Fine-Tuning variant and a parameter-efficient LoRA-based variant that updates only a small fraction of the model parameters. We present a complete development pipeline, including data collection and curation, model fine-tuning, benchmark design and LLM-judge choice, evaluation, and deployment. Through this work, we demonstrate that our training strategy enables improvements in domain relevance and performance without the need for large-scale infrastructure. By evaluating the performance of both EnergyGPT variants using domain-specific question-answering benchmarks, our results show that the adapted models consistently outperform the base model in most energy-related language understanding and generation tasks, with the LoRA variant achieving competitive gains at significantly reduced training cost.

Paper Structure

This paper contains 53 sections, 11 equations, 16 figures, 16 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. SFT for Domain Adaptation of LLMs
  4. Other Cost Efficient Methods for Domain Adaptation of LLMs
  5. Other Strategies for Knowledge-Intensive NLP
  6. Data Preparation: Methodology
  7. Data Collection
  8. Data Cleaning: Energy-related Scientific Literature
  9. Data Curation: The Pile
  10. Quality Filtering
  11. Exact De-duplication
  12. Fuzzy De-duplication
  13. Semantic Filtering
  14. Data Balancing
  15. Data Pairs Generation for Supervised Fine-Tuning
  16. ...and 38 more sections

Figures (16)

  • Figure 1: Generalizable pipeline for building domain-specialized assistants.
  • Figure 2: The Pile data processing pipeline.
  • Figure 3: Data preparation pipeline for fine-tuning EnergyGPT.
  • Figure 4: EnergyGPT pipeline.
  • Figure 5: Cohen's kappa between selected candidate LLM judges and the mean human scores across the seven evaluation dimensions.
  • ...and 11 more figures