The field of human-AI interaction is rapidly evolving, with a focus on improving the capabilities of large language models (LLMs) to understand and respond to user queries. Recent research has highlighted the importance of prompt rewriting, metacognition, and uncertainty quantification in enhancing the performance of LLMs. Studies have shown that rephrasing ineffective prompts can elicit better responses from conversational systems, while preserving the user's original intent. Additionally, the development of neural symbolic frameworks and attention-based methods has improved the ability of LLMs to model interactions and quantify uncertainty. Noteworthy papers in this area include the introduction of SUNAR, a novel approach to neighborhood-aware retrieval for complex question answering, and the development of ECLAIR, a multi-agent framework for interactive disambiguation. Furthermore, research on preference-based learning and retrieval-augmented generation has shown promise in improving conversational question answering. Overall, the field is moving towards more sophisticated and human-like interactions between users and LLMs.