Towards EnergyGPT: A Large Language Model Specialized for the Energy Sector
This work addresses the need for precise domain knowledge in the energy sector, though it is incremental as it builds on existing fine-tuning methods.
The paper tackled the problem of general-purpose large language models being ineffective in the specialized energy sector by introducing EnergyGPT, a model fine-tuned from LLaMA 3.1-8B, which outperformed the base model in most energy-related tasks as demonstrated through domain-specific benchmarks.
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 LLaMA 3.1-8B model using Supervised Fine-Tuning on a high-quality, curated corpus of energy-related texts. 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 the model using domain-specific question-answering benchmarks, our results demonstrate that EnergyGPT outperforms the base model in most of the energy-related language understanding and generation tasks.