The field of natural language processing is witnessing significant advancements in the development of large language models (LLMs) and their applications in various areas, including story continuation and movie script generation. Recent studies have shown that LLMs are capable of preserving semantic isotopies in story continuations, demonstrating their ability to understand and generate coherent texts. Furthermore, researchers have proposed frameworks for evaluating and enhancing LLM-powered movie script generation, highlighting the importance of nuanced storytelling and emotional depth in cinematic scripts. In the realm of open-source software development, empirical studies have investigated the proposal process, shedding light on the reasons behind proposal decline and offering insights into improving contributor experience and reviewer workload. Noteworthy papers in this area include:

• A study on Large Language Models that preserve semantic isotopies in story continuations, demonstrating their ability to generate coherent texts.
• CML-Bench, a framework for evaluating and enhancing LLM-powered movie script generation, which effectively assigns high scores to well-crafted human-written scripts.
• An empirical study on declined proposals in open-source software development, which identified key reasons for proposal decline and demonstrated the potential of large language models in predicting decline decisions.