用大模型打造沉浸式面试模拟平台，支持多模态评估与个性化反馈。
PolyInterview: An LLM-based Platform for Immersive Mock Interview Practice with Comprehensive Multimodal Assessment
- 基于岗位描述和简历生成定制化问题，支持语音对话与追问。
- 13项行为特征评估，产出10个维度评分与双轨能力追踪。
- 适合求职者练手、培训师教学，反馈有证据支撑且可操作。
求职面试准备对获得理想职位至关重要，但真实练习机会少、专业辅导成本高，自测又缺乏动态互动和结构化评估。现有系统多仅解决部分需求，如固定题库、单一沟通方式或缺乏依据的反馈。我们提出PolyInterview，一个基于大语言模型的沉浸式模拟面试平台，能根据目标岗位描述和简历生成定制化问题，通过唇同步数字人进行多轮口语对话，并支持答案感知的追问。系统从内容、语调、非语言行为三方面评估表现，由四个并行评估器提取13项行为级特征，聚合为10个评估维度和两个能力轨道。评估报告基于KSA与STAR框架，将每项得分关联具体行为证据并提供改进建议。平台已公开，当前数据包含101个账户、1,564次面试会话、7,665个生成问题及1,422套五阶段问题集。93.7%的会话中，生成问题与匹配岗位描述的契合度高于跨岗位描述。十位专家评估认为问题设计合理，反馈具有实用性。
原文摘要 · Abstract (English)
Preparing for job interviews is important for securing desired positions, yet realistic practice remains difficult to access: real interviews are infrequent, expert mock coaching is costly, and self-practice offers neither adaptive dialogue nor structured assessment. Existing systems typically address only parts of this need through fixed question sequences, limited communication channels, or feedback with little supporting evidence. We present PolyInterview, an LLM-based platform for immersive mock interview practice with comprehensive multimodal assessment. PolyInterview uses the target job description and CV to generate questions tailored to the role and candidate, conducts multi-turn spoken interviews with a lip-synced digital human interviewer that asks answer-aware follow-up questions, and evaluates response content, vocal delivery, and non-verbal behavior. Four parallel evaluators produce 13 behavior-level features that are aggregated into 10 assessment aspects and two competency tracks. Guided by the KSA and STAR frameworks, the report links each score to behavioral evidence and actionable recommendations. PolyInterview is publicly accessible. Its current all-account snapshot contains 101 accounts, 1,564 interview sessions, 7,665 generated questions, and 1,422 five-stage question sets. Generated questions are more closely aligned with their matched job description than with cross-role job descriptions in 93.7% of sessions. An evaluation by ten experts found strong question plans and actionable feedback.
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