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

arXiv: https://arxiv.org/abs/2412.16943 · HTML: https://mlsanity.com/paper/2412.16943/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 22 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations](https://mlsanity.com/paper/2005.08866/) (2020)
- [ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?](https://mlsanity.com/paper/2306.01386/) (2023)
- [What should I Ask: A Knowledge-driven Approach for Follow-up Questions Generation in Conversational Surveys](https://mlsanity.com/paper/2205.10977/) (2022)
- [Linguistically-Enriched and Context-Aware Zero-shot Slot Filling](https://mlsanity.com/paper/2101.06514/) (2021)

## Built on by (most cited first, 3 of 3)

- [Stephanie2: Thinking, Waiting, and Making Decisions Like Humans in Step-by-Step AI Social Chat](https://mlsanity.com/paper/2601.05657/) (2026)
- [Beyond the Resum'e: A Rubric-Aware Automatic Interview System for Information Elicitation](https://mlsanity.com/paper/2603.01775/) (2026)
- [Generating Diverse Personas for User Simulators to Test Interview Dialogue Systems](https://mlsanity.com/paper/2608.19549/) (2026)

## Abstract (arXiv)

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.