A Career Interview Dialogue System using Large Language Model-based Dynamic Slot Generation

TL;DR

This paper presents a novel dialogue system for career interviews that uses large language models to dynamically generate interview slots, improving flexibility and naturalness over traditional fixed-slot systems.

Contribution

It introduces a LLM-based method with abduction for dynamic slot generation, enhancing interview dialogue flexibility and naturalness.

Findings

Improved information collection in simulated interviews.

Enhanced dialogue naturalness with abduction-based slot generation.

Validated effectiveness through user simulator experiments.

Abstract

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.