ArXivPaperRating-LLM is an exploratory tool that utilizes a large language model (LLM) to evaluate newly published research papers in the computer science field on arXiv. It focuses on papers related to machine learning, AI, and reinforcement learning, assessing them based on innovation, newness, potential, clarity, and relevance solely from their titles and summaries.
As the number of publications in computer science burgeons, identifying impactful research efficiently becomes increasingly challenging. This project is an initial foray into how LLMs can aid in the preliminary assessment of academic literature, serving as a foundation for studying AI's role in academic evaluations and its potential biases.
The assessment is performed through prompt engineering with an LLM, which currently reviews only the title and summary of each paper. The simplicity of this approach is acknowledged, with plans to extend the analysis to full texts in future iterations.
This project is in its infancy and will undergo significant development. Future versions will aim to refine the evaluation process and expand the model's understanding of academic content.