Large Language Model-Based Intelligent Academic Research Assistant for Scientific Literature Analysis
Abstract
The exponential growth of scientific publications presents significant challenges for researchers in efficiently identifying, summarizing, and synthesizing relevant literature. This paper proposes an intelligent academic research assistant powered by Large Language Models (LLMs) integrated with Retrieval-Augmented Generation (RAG) and knowledge graph technologies. The proposed framework retrieves relevant scholarly documents from multiple scientific repositories, performs semantic analysis, extracts key findings, identifies research gaps, and generates structured literature reviews with proper citation support. A ranking mechanism based on citation networks and semantic relevance improves document retrieval quality. Experimental evaluation demonstrates enhanced literature search efficiency, higher summary quality, and improved research productivity compared with conventional search systems. The proposed framework supports researchers by accelerating systematic literature reviews and evidence-based scientific writing.
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