题　目： LLM-Measure: Generating Valid, Consistent, and Reproducible Text-Based Measures for Business Research

嘉　宾： Hanyu Duan（段涵宇）, Ph.D. Candidate，HKUST

主持人：韩雪雯，助理教授，上海交通大学安泰经济与管理学院

时　间：2026年12月8日（周二）10:00-11:30

地　点：上海交通大学安泰经济与管理学院包兆龙图书馆A303

内容简介:

Text is data. In business research, text data are increasingly used to construct measures that are included as variables in econometric models. Recent advances in large language models (LLMs) have made this practice easier, enabling researchers to create text based measures simply by prompting an LLM. Yet this convenience comes with an important but often-overlooked issue: the resulting measures can be highly sensitive to prompt wording. Even seemingly superficial changes to a prompt can yield materially different measures, raising concerns about measurement reliability and reproducibility and, more critically, about the credibility of subsequent research findings. In this work, we call attention to this issue and develop LLM-Measure, an approach that uses an LLM’s latent representations to construct text-based measures with reduced prompt sensitivity. Across three measurement tasks, the measures produced by LLM-Measure align more closely with human judgments and remain more consistent across prompt rephrasings than those produced through conventional LLM prompting. To demonstrate LLM Measure’s practical value in supporting empirical research, we use it to construct measures at the macro, firm, and individual levels and incorporate them into relevant econometric models, successfully replicating findings documented in prior research. Overall, this work addresses a pressing methodological issue in LLM-assisted research and offers a broadly applicable method for text-based measurement within and beyond information systems.

演讲人简介：

Hanyu Duan is a PhD candidate in the Department of Information Systems, Business Statistics and Operations Management at Hong Kong University of Science and Technology (HKUST). He received his MSc in data-driven modeling from HKUST in 2020 and his BSc in applied physics from Jimei University in 2019. His research focuses on developing natural language processing-based computational methods to study problems in business and the social sciences.