自动化多轮面试（MMI）评分
Automated Multiple Mini Interview (MMI) Scoring
AI总结：
本文提出多智能体提示框架，通过转录优化和标准特定评分提升MMI评分的准确性和可靠性，优于传统微调方法。
摘要：
在竞争性选拔过程中，评估如共情、伦理判断和沟通能力等软技能至关重要，但人类评分往往不一致且有偏见。尽管大型语言模型（LLMs）已提升了自动作文评分（AES），但我们显示，最先进的基于理由的微调方法在处理多轮面试（MMIs）的抽象、依赖上下文的性质时存在困难，无法捕捉候选人叙述中隐含的信号。我们引入了一个多智能体提示框架，将评估过程分解为转录优化和标准特定评分。使用3次上下文学习与大型指令微调模型，我们的方法在平均QWK指标上优于专门微调的基线（0.62 vs 0.32），并实现了与人类专家相当的可靠性。我们进一步在ASAP基准上展示了框架的通用性，其中它在不额外训练的情况下与领域专用的最新模型相当。这些发现表明，对于复杂的、主观的推理任务，结构化的提示工程可能为数据密集型微调提供可扩展的替代方案，改变LLM在自动化评估中的应用方式。
英文摘要：
Assessing soft skills such as empathy, ethical judgment, and communication is essential in competitive selection processes, yet human scoring is often inconsistent and biased. While Large Language Models (LLMs) have improved Automated Essay Scoring (AES), we show that state-of-the-art rationale-based fine-tuning methods struggle with the abstract, context-dependent nature of Multiple Mini-Interviews (MMIs), missing the implicit signals embedded in candidate narratives. We introduce a multi-agent prompting framework that breaks down the evaluation process into transcript refinement and criterion-specific scoring. Using 3-shot in-context learning with a large instruct-tuned model, our approach outperforms specialised fine-tuned baselines (Avg QWK 0.62 vs 0.32) and achieves reliability comparable to human experts. We further demonstrate the generalisability of our framework on the ASAP benchmark, where it rivals domain-specific state-of-the-art models without additional training. These findings suggest that for complex, subjective reasoning tasks, structured prompt engineering may offer a scalable alternative to data-intensive fine-tuning, altering how LLMs can be applied to automated assessment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
