An Interview System Based on Large Language Models and Multi-Agent Interactions
Quan Xinyue, Yao Zhang, Xie Xiang, Ya Wang · 2024
To address the increasing demand for interviews, we have proposed an intelligent interview system. This system has the capability to generate interview questions and design interview processes. This system introduces a ChatGPT-based iterative enhancement algorithm for seed datasets to create a high-quality prompt-question dataset specific to the interview domain. Through large-scale language model fine-tuning, the system can generate more accurate and expected interview questions, demonstrating significant performance improvements across multiple evaluation metrics. Additionally, the study introduces an innovative multi-agent interaction algorithm to enhance interview process efficiency and comprehensive information gathering. Experimental results indicate improvements in text coherence and contextual understanding with the fine-tuned model, and subjective testing confirms the system's fluency and practicality.