When assessing the health of older adults, oral interviews and written questionnaires are commonly used. However, these methods are time-consuming in terms of both execution and data aggregation. To address this issue, systems utilizing generative AI for health information collection through conversation have been developed and implemented. Despite these advancements, the motivation of older adults to consistently engage with such systems in their daily lives has not been thoroughly explored. In this study, we developed a smart-speaker extension that uses generative AI to monitor health status through casual conversations with older adult users. The system was tested in a two-week home trial with older adult participants. We conducted post-trial questionnaires and interviews, and we analyzed conversation log data. The results revealed that older adult users enjoy interacting with such systems and can integrate their use into their daily routines. Customized notifications through text messages encouraged system use, and the system’s ability to refer to previous conversations and address users by name was identified as a key factor motivating continued use.

Survey research using open-ended responses is an important method thatcontributes to the discovery of unknown issues and new needs. However,survey research generally requires time and cost-consuming manual dataprocessing, indicating that it is difficult to analyze large dataset.To address this issue, we propose an LLM-based method to automate partsof the grounded theory approach (GTA), a representative approach of thequalitative data analysis. We generated and annotated pseudo open-endedresponses, and used them as the training data for the coding proceduresof GTA. Through evaluations, we showed that the models trained withpseudo open-ended responses are quite effective compared with thosetrained with manually annotated open-ended responses. We alsodemonstrate that the LLM-based approach is highly efficient andcost-saving compared to human-based approach.

In this position paper, I present my research interests regarding the dialogue systems that can reflect the interlocutor’s values, such as their way of thinking and perceiving things. My work focuses on two main aspects: dialogue systems for eliciting the interlocutor’s values and methods for understanding the interlocutor’s values from narratives. Additionally, I discuss the abilities required for Spoken Dialogue Systems (SDSs) that can converse with the same user multiple times. Finally, I suggest topics for discussion regarding an SDS as a personal assistant for everyday use.

The purpose of this paper is to construct a model for the generation of sophisticated headlines pertaining to stock price fluctuation articles, derived from the articles’ content. With respect to this headline generation objective, this paper solves three distinct tasks: in addition to the task of generating article headlines, two other tasks of extracting security names, and ascertaining the trajectory of stock prices, whether they are rising or declining. Regarding the headline generation task, we also revise the task as the model utilizes the outcomes of the security name extraction and rise/decline determination tasks, thereby for the purpose of preventing the inclusion of erroneous security names. We employed state-of-the-art pre-trained models from the field of natural language processing, fine-tuning these models for each task to enhance their precision. The dataset utilized for fine-tuning comprises a collection of articles delineating the rise and decline of stock prices. Consequently, we achieved remarkably high accuracy in the dual tasks of security name extraction and stock price rise or decline determination. For the headline generation task, a significant portion of the test data yielded fitting headlines.