Abstract: This research paper explores the development of an application leveraging fine-tuned Large Language Models (LLMs) for advanced interaction with research papers and GitHub repositories. Traditional methods often involve manual effort or siloed tools, lacking integrated insights across textual and code-based resources. Our system bridges this gap, enabling developers and researchers to analyze papers, understand implementations, and create adaptations seamlessly. By fine-tuning a pre-trained LLM on research papers and source code, and integrating Retrieval-Augmented Generation (RAG) techniques, the system delivers contextual and dynamic interactions. Unlike previous approaches focusing on isolated analysis or summarization, our unified platform combines academic and practical insights, helping users navigate complex topics, validate claims, and prototype ideas efficiently. This work addresses key challenges in integrating text and code for knowledge discovery, setting the stage for enhanced research and development workflows.
Keywords: Large Language Models, AI-Powered Code Understanding, Codebase Analysis, Intelligent Document Processing, Automated Literature Review, Contextual Code Retrieval.
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DOI:
10.17148/IJARCCE.2025.14371
