Review Generation

Automated review generation is rapidly evolving, aiming to create high-quality, personalized reviews for various applications, from product recommendations to scientific literature analysis. Current research heavily utilizes large language models (LLMs), often enhanced with techniques like prompt engineering, retrieval-augmented generation, and multi-agent approaches, to address challenges such as hallucination, generic outputs, and ensuring factual accuracy. This field significantly impacts scientific productivity by automating literature review processes and improving the efficiency of peer review, while also enhancing recommender systems through more informative and personalized explanations for users.