Dialogue System using Large Language Model-based Dynamic Slot Generation (2025)

Abstract: In this position paper, I present my research interests in dialogue systems that elicit user career-related information. My work centres on two aspects. First, I seek to enhance the information-gathering capability of task-oriented systems by using large language models (LLMs) to generate slots dynamically, enabling the system to ask for deeper career details, such as reasons for leaving a job. Second, I propose a method—planned for future study—that decomposes and recomposes system questions along a “depth” axis so that sensitive information can be obtained more naturally. Finally, I discuss the positive and negative implications of combining LLMs with spoken dialogue systems (SDSs) and consider how SDS technology will interact with society.

## research interests my research interests are building a spoken dialogue system (sds) that engages deeply with users' "sense of values," particularly in domains such as career interviews. to realize the system, i focus on two main areas: enhancing the information collecting abilities of slot-filling dialogue systems (sfd systems) using large language models (llms), and exploring the potential of sds in supporting career development. ## spoken dialogue system (sds) research dialogue research in 5 to 10 years recent advances in llms have dramatically boosted the performance of sdss. over the next five to ten years, sdss will incorporate long-term conversational history and psychological insights, enabling far richer user understanding and more human-like interaction. as such systems become widely deployed, the resulting accumulation of dialogue data and user feedback should create a virtuous cycle that further accelerates performance gains. at the same time, the very need for dialogue is beginning to waver. thanks to llms, benefits that once required conversational exchange, information, reassurance, and a sense of conviction can increasingly be delivered through non-dialogue channels. if this trend continues, dialogue may be dismissed as costly, risking weaker social bonds and more isolated decision-making. for this reason, future dialogue research must confront the fundamental questions: why do we converse? what distinctive value does dialogue offer? and how can we protect and amplify that value in an age where non-dialogue alternatives proliferate? societal deployment and evaluation of sdss traditional dialogue-system research has focused on technical completeness, concentrating on goal-oriented dia-logues and evaluating performance with quantitative metrics such as slot-filling accuracy and intent matching. with the rise of large language models (llms) that let almost anyone build high-functioning systems, there is an increasing need for new evaluation frameworks prioritizing user experience in real-world scenarios. the dialogue system we are developing serves as a component for collecting information from nurses during careercounseling interviews; we therefore place particular emphasis on system design and evaluation within a broader social context. therefore, our works also emphasize system design and evaluation in a broader social context. based on this perspective, we believe that dialogue systems require multi-layer evaluation, not only from the standpoint of end users (e.g., nurses) but also from other stakeholders such as system deployers (e.g., hospitals) and system designers (e.g., dialogue-system researchers). although it may not be necessary to assess all of these layers at once, considering them can help prevent structural misalignments, for example, optimizing for shortterm user satisfaction at the expense of longer-term impacts on other layers.