A Career Interview Dialogue System using Large Language Model-based Dynamic Slot Generation
This work addresses the problem of inefficient and low-quality career interviews for nursing managers, representing an incremental improvement in dialogue systems.
The study tackled the inflexibility of conventional slot-filling dialogue systems in career interviews by proposing a method that uses large language models to dynamically generate slots based on dialogue flow, incorporating abduction for more appropriate slot generation. Experiments with a user simulator showed that this approach enhanced information-collecting capabilities and dialogue naturalness.
This study aims to improve the efficiency and quality of career interviews conducted by nursing managers. To this end, we have been developing a slot-filling dialogue system that engages in pre-interviews to collect information on staff careers as a preparatory step before the actual interviews. Conventional slot-filling-based interview dialogue systems have limitations in the flexibility of information collection because the dialogue progresses based on predefined slot sets. We therefore propose a method that leverages large language models (LLMs) to dynamically generate new slots according to the flow of the dialogue, achieving more natural conversations. Furthermore, we incorporate abduction into the slot generation process to enable more appropriate and effective slot generation. To validate the effectiveness of the proposed method, we conducted experiments using a user simulator. The results suggest that the proposed method using abduction is effective in enhancing both information-collecting capabilities and the naturalness of the dialogue.