The field of natural language processing is moving towards more personalized and fine-grained approaches to text generation and evaluation. Researchers are exploring ways to incorporate user preferences and stylistic characteristics into language models, enabling more diverse and controllable text generation. This shift is driven by the need for more effective and efficient evaluation metrics, as well as the development of new architectures that can capture the complexities of human language. Notably, the use of diffusion models and syntax-guided approaches is becoming increasingly popular. Some noteworthy papers in this area include: PerQ, which introduces a computationally efficient method for evaluating personalization quality of generated text. Syntax-Guided Diffusion Language Models with User-Integrated Personalization, which proposes a novel architecture that integrates structural supervision and personalized conditioning to enhance text quality and diversity.