The fields of caregiving, autonomous systems, robotics, and large language models are experiencing significant advancements, driven by the increasing use of large language models (LLMs) to deliver empathetic and tailored support, improve perception and decision-making capabilities, and enhance navigation and control tasks. A common theme among these research areas is the exploration of LLMs to generate diverse datasets, improve user requests, and provide emotional support. Notable papers include a study on a large language model-powered conversational agent that delivers Problem-Solving Therapy for family caregivers, and the introduction of AgentSense, a virtual data generation pipeline that leverages LLMs to create daily routines and action sequences for simulated home environments. Additionally, research on ASMR, a framework that uses large generative models to simulate conversations and environmental contexts for robotic action reflection, has achieved state-of-the-art performance in multimodal classification tasks. In the field of autonomous systems, the use of LLMs and vision-language models has enabled more accurate and robust perception, planning, and decision-making capabilities. The development of grounded vision-language planning models and teleoperation systems has also improved navigation and control tasks in robotics. Furthermore, LLMs have been used to simulate human behavior in social science contexts, such as voting behavior and survey responses, but concerns about their potential role in exacerbating ideological polarization have been raised. Researchers are also exploring methods to enhance the confidence estimation and robustness of LLMs, including data augmentation strategies and robustness evaluation techniques. Overall, the integration of LLMs in various research areas has the potential to revolutionize the way we approach complex tasks and improve the safety, efficiency, and reliability of various systems.