Comparative Analysis of Automatic Literature Review Using Mistral Large Language Model and Human Reviewers

Abstract This study evaluates the effectiveness of the Mistral Large Language Model (LLM), enhanced with Retrieval-Augmented Generation (RAG), in automating the process of conducting literature reviews, comparing its performance with traditional human-led review processes. Through a methodical analysis of 50 scientific papers from the OpenReview platform, the study investigates the model's efficiency, scalability, and quality of review, including coherence, relevance, and analytical depth. The findings indicate that while the Mistral LLM significantly surpasses human efforts in terms of efficiency and scalability, it occasionally lacks the analytical depth and attention to detail that characterize human reviews. Despite these limitations, the model demonstrates considerable potential in standardizing preliminary literature reviews, suggesting a hybrid approach where Mistral LLM's capabilities are integrated with human expertise to enhance the literature review process. The study underscores the necessity for further advancements in AI technology to achieve deeper analytical insights and highlights the importance of addressing ethical concerns and biases in AI-assisted research. The integration of LLMs like Mistral presents a promising avenue for redefining academic research methodologies, pointing towards a future where AI and human intelligence collaborate to advance scholarly discourse.