The field of natural language processing is witnessing a significant shift towards the development and application of large language models (LLMs) in various domains. Recent research has focused on enhancing the capabilities of LLMs, including their ability to generate previews for long-form content, estimate their knowledge capacity, and evaluate their unseen capabilities. Additionally, there is a growing interest in developing reusable and scalable recommender systems that can handle diverse tasks without significant reconfiguration. Noteworthy papers in this area include the proposal of an LLM-based approach for generating podcast episode previews, which demonstrated a significant reduction in the need for meticulous feature engineering and a 4.6% increase in user engagement. Another notable paper introduced KnowSum, a statistical framework for evaluating the unseen knowledge of LLMs, which revealed that a substantial volume of knowledge is omitted when relying solely on observed LLM performance. The development of universal and reusable recommender systems, such as the Dataset- and Task-Independent Recommender System (DTIRS), is also gaining traction. Furthermore, the application of generative pretraining to discriminative recommendation tasks has shown promising results, with the proposed GPSD framework delivering superior performance and narrowing the generalization gap in model training.