讲座题目：This Candidate is [MASK]: Letters of Reference and Job Market Outcomes using LLMs（这位候选人是[MASK]：利用大语言模型预测推荐信对求职结果的影响）

主讲人：Fabian Slonimczyk   莫斯科高等经济学院

讲座时间：2025年07月03日10:00

讲座地点：学院440

讲座内容摘要：

I implement a prompt-based learning strategy to extract measures of sentiment and other features from confidential reference letters. I show that the contents of reference letters are clearly reflected in the performance of job market candidates in the Economics academic job market. In contrast, applying traditional “bag-of-words” approaches produces measures of sentiment that, while positively correlated to my LLM-based measure, are not predictive of job market outcomes. Using a random forest, I show that both letter quality and length are predictive of success in the job market. Letters authored by advisers appear to be as important as those written by other referees.

本文采用基于提示词的学习策略，从保密的推荐信中提取情感倾向和其他特征指标。研究证明，在经济学学术求职市场中，推荐信的内容能显著反映候选人的实际表现。相比之下，传统的“词袋”分析方法测度的情感倾向虽与基于大语言模型测度的情感倾向呈正相关，却无法有效预测求职结果。通过随机森林模型分析发现，推荐信的质量与长度均能很好地预测求职成功率。导师撰写的推荐信与其他推荐人撰写的信件具有同等重要性。

主讲人学术简介：

Fabian Slonimczyk，莫斯科高等经济学院长聘副教授，2009年获美国马萨诸塞大学阿默斯特分校经济学博士学位，并拥有波士顿大学软件开发（数据科学）硕士学位。研究领域主要为劳动经济学、发展经济学和应用计量经济学。研究论文发表在European Economic Review、Labour Economics、Journal of Economic Behavior & Organization、Journal of Economic Inequality、Economics of Transition、Research in Labor Economics等国际期刊。