Abstract
Abstract Large Language Models (LLMs) are showing increasingly versatile applications. Locally hosted, open-source models are becoming favored for a wide use spectrum. These models offer offline, customized knowledge repositories with enhanced privacy and security. This document presents a roadmap for creating a dependable LLM specifically for petroleum engineering and energy queries, integrating years of accumulated knowledge while maintaining data confidentiality. LLMs aid in deeply comprehending complicated topics via interactive education, and they are efficient in summarizing and analyzing extensive academic texts. Additionally, the paper delves into the subtleties of smaller-scale offline models, comparing their results, and capabilities with those of commercial alternatives. The methodology in this paper illustrates an example that involves utilizing a wealth of data from the University of Houston's resources, encompassing professors’ video and audio lectures, published papers, exams, academic texts, and research projects. It processes this data through a sophisticated machine-learning pipeline to assess various embedding models and test different data chunk sizes. The goal is to optimize information extraction for relevance and significance. This involves benchmarking various LLM spectra against commercial models. The LLM is then fine-tuned for chat tasks using multiple real and synthetic datasets to enhance stability in addition to another stage of fine-tuning utilizing petroleum-engineering questions and answers. The final goal of the study is to compile UH Department of Petroleum Eng. materials, integrating lecture notes with actual datasets. This blend saves time for students and enhances research skills for both students and professors. Advantages include enhanced data security through local installations, preventing data leaks. Financial efficiency is realized by relying on internal infrastructure. It reduces the latency and boosts user engagement without additional expenses. The study highlights the proficiency of smaller LLMs in tasks like text generation and summarization, surpassing their larger counterparts even on limited hardware. Their effectiveness makes them an optimal, economical, and secure option for educational and petroleum industry applications. This paper outlines a strategy for entities unable to bear the expense of commercial cloud-based LLMs, to implement their own tailored model in-house. It adeptly addresses the complexities of safeguarding data, detailing a journey from data acquisition through a sophisticated processing system to the meticulous development and tuning of the model. The paper highlights the impact of LLMs on academic fields, particularly in petroleum engineering. It focuses on the critical role of privacy and security in the development of these models. The results highlight noteworthy progress in research productivity and educational engagement, indicating a future where LLMs play a pivotal role in shaping educational strategies within specialized domains for companies.
Published Version
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