  The emergence of online recruitment services has revolutionized the
traditional landscape of job seeking and recruitment, necessitating the
development of high-quality industrial applications to improve person-job
fitting. Existing methods generally rely on modeling the latent semantics of
resumes and job descriptions and learning a matching function between them.
Inspired by the powerful role-playing capabilities of Large Language Models
(LLMs), we propose to introduce a mock interview process between LLM-played
interviewers and candidates. The mock interview conversations can provide
additional evidence for candidate evaluation, thereby augmenting traditional
person-job fitting based solely on resumes and job descriptions. However,
characterizing these two roles in online recruitment still presents several
challenges, such as developing the skills to raise interview questions,
formulating appropriate answers, and evaluating two-sided fitness. To this end,
we propose MockLLM, a novel applicable framework that divides the person-job
matching process into two modules: mock interview generation and two-sided
evaluation in handshake protocol, jointly enhancing their performance through
collaborative behaviors between interviewers and candidates. We design a
role-playing framework as a multi-role and multi-behavior paradigm to enable a
single LLM agent to effectively behave with multiple functions for both
parties. Moreover, we propose reflection memory generation and dynamic prompt
modification techniques to refine the behaviors of both sides, enabling
continuous optimization of the augmented additional evidence. Extensive
experimental results show that MockLLM can achieve the best performance on
person-job matching accompanied by high mock interview quality, envisioning its
emerging application in real online recruitment in the future.
