专为能源领域打造的LLaMA模型，高效精准回答专业问题。
Towards EnergyGPT: A Large Language Model Specialized for the Energy Sector
- 用能源文本微调LLaMA 3.1-8B，分全参数与轻量LoRA两种方式。
- 在能源问答任务中，改进效果显著，LoRA版成本更低。
- 适合能源行业从业者、研究者快速获取专业信息。
大语言模型在多个领域展现强大能力，但通用特性限制了其在能源等专业领域的应用，因能源领域需要深度技术知识与精确领域理解。本文提出EnergyGPT，一个针对能源行业的专用语言模型，基于LLaMA 3.1-8B通过高质量、精心筛选的能源文本进行微调。采用两种适配策略：全参数监督微调与仅更新少量参数的参数高效LoRA方法。构建完整开发流程，涵盖数据收集与清洗、模型微调、基准设计、LLM裁判选择、评估与部署。实验表明，该训练策略在无需大规模基础设施的情况下提升了模型在能源相关任务中的相关性与性能。在领域特定问答基准测试中，两个变体均显著优于基础模型，其中LoRA版本以极低训练成本实现竞争力提升。
原文摘要 · Abstract (English)
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.
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