用大模型模拟面试官，精准评估候选人潜质
Beyond the Resumé: A Rubric-Aware Automatic Interview System for Information Elicitation
- 用大模型动态追问，基于评分标准提取候选人深层能力
- 模拟面试中信念值收敛到预设能力水平，验证有效性
- 适合想提升初筛质量的HR或招聘系统开发者
高效招聘对组织成功至关重要，但专家评审（如技术主管面试）难以规模化。当前自动化简历评分等方法仅依赖有限信息进行粗略筛选。本文提出利用大语言模型（LLM）扮演领域专家，低成本获取候选人精细、岗位特定的能力信息，从而提升早期决策质量。我们构建了一个系统，通过LLM面试官以校准方式更新对申请人评分标准导向的潜在特质信念。在模拟面试上评估显示，信念值能收敛至人工构造的潜能力水平。代码、一个公开匿名简历数据集、信念校准测试及模拟面试数据已开源（https://github.com/mbzuai-nlp/beyond-the-resume），演示地址为 https://btr.hstu.net。
原文摘要 · Abstract (English)
Effective hiring is integral to the success of an organisation, but it is very challenging to find the most suitable candidates because expert evaluation (e.g.\ interviews conducted by a technical manager) are expensive to deploy at scale. Therefore, automated resume scoring and other applicant-screening methods are increasingly used to coarsely filter candidates, making decisions on limited information. We propose that large language models (LLMs) can play the role of subject matter experts to cost-effectively elicit information from each candidate that is nuanced and role-specific, thereby improving the quality of early-stage hiring decisions. We present a system that leverages an LLM interviewer to update belief over an applicant's rubric-oriented latent traits in a calibrated way. We evaluate our system on simulated interviews and show that belief converges towards the simulated applicants' artificially-constructed latent ability levels. We release code, a modest dataset of public-domain/anonymised resumes, belief calibration tests, and simulated interviews, at \href{https://github.com/mbzuai-nlp/beyond-the-resume}{https://github.com/mbzuai-nlp/beyond-the-resume}. Our demo is available at \href{https://btr.hstu.net}{https://btr.hstu.net}.
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