  This research explores the integration of large language models (LLMs) into
scientific data assimilation, focusing on combustion science as a case study.
Leveraging foundational models integrated with Retrieval-Augmented Generation
(RAG) framework, the study introduces an approach to process diverse combustion
research data, spanning experimental studies, simulations, and literature. The
multifaceted nature of combustion research emphasizes the critical role of
knowledge processing in navigating and extracting valuable information from a
vast and diverse pool of sources. The developed approach minimizes
computational and economic expenses while optimizing data privacy and accuracy.
It incorporates prompt engineering and offline open-source LLMs, offering user
autonomy in selecting base models. The study provides a thorough examination of
text segmentation strategies, conducts comparative studies between LLMs, and
explores various optimized prompts to demonstrate the effectiveness of the
framework. By incorporating an external database, the framework outperforms a
conventional LLM in generating accurate responses and constructing robust
arguments. Additionally, the study delves into the investigation of optimized
prompt templates for the purpose of efficient extraction of scientific
literature. The research addresses concerns related to hallucinations and false
research articles by introducing a custom workflow developed with a detection
algorithm to filter out inaccuracies. Despite identified areas for improvement,
the framework consistently delivers accurate domain-specific responses with
minimal human oversight. The prompt-agnostic approach introduced holds promise
for future deliberations. The study underscores the significance of integrating
LLMs and knowledge processing techniques in scientific research, providing a
foundation for advancements in data assimilation and utilization.
