The field of human-language model interaction and online discourse is rapidly evolving, with a focus on improving the robustness and effectiveness of language models in various applications. Recent research has highlighted the importance of adapting language models to the communication style shift that occurs when interacting with humans, and has explored strategies such as data augmentation and inference-time user message reformulation to enhance model performance. Additionally, there is a growing interest in developing systems that foster collective discourse and encourage balanced and inclusive discussions online. Noteworthy papers in this area include: Mind the Gap: Linguistic Divergence and Adaptation Strategies in Human-LLM Assistant vs. Human-Human Interactions, which presents empirical evidence of distinct communication styles when interacting with chatbots versus human agents. A Computational Framework for Interpretable Text-Based Personality Assessment from Social Media, which develops a framework for personality assessment that delivers assessments comparable to human evaluations while maintaining high interpretability and efficiency.