NANOGPT: A Query-Driven Large Language Model Retrieval-Augmented Generation System for Nanotechnology Research

TL;DR

This paper introduces NANOGPT, a retrieval-augmented language model system designed to improve literature review efficiency in nanotechnology research by integrating multiple scholarly sources and advanced query mechanisms.

Contribution

The paper presents a novel LLM-RAG system tailored for nanotechnology, combining diverse data sources and advanced retrieval techniques to enhance research assistance.

Findings

Reduces literature review time significantly

Maintains high accuracy and relevance in retrieval

Outperforms standard language models in domain-specific tasks

Abstract

This paper presents the development and application of a Large Language Model Retrieval-Augmented Generation (LLM-RAG) system tailored for nanotechnology research. The system leverages the capabilities of a sophisticated language model to serve as an intelligent research assistant, enhancing the efficiency and comprehensiveness of literature reviews in the nanotechnology domain. Central to this LLM-RAG system is its advanced query backend retrieval mechanism, which integrates data from multiple reputable sources. The system retrieves relevant literature by utilizing Google Scholar's advanced search, and scraping open-access papers from Elsevier, Springer Nature, and ACS Publications. This multifaceted approach ensures a broad and diverse collection of up-to-date scholarly articles and papers. The proposed system demonstrates significant potential in aiding researchers by providing a streamlined, accurate, and exhaustive literature retrieval process, thereby accelerating research advancements in nanotechnology. The effectiveness of the LLM-RAG system is validated through rigorous testing, illustrating its capability to significantly reduce the time and effort required for comprehensive literature reviews, while maintaining high accuracy, query relevance and outperforming standard, publicly available LLMS.