Posts by Collection

Automation of Text-Based Economic Indicator Construction: A Pilot Exploration on Economic Policy Uncertainty Index

Published in In Proceedings of The 33rd ACM International Conference on Information and Knowledge Management, 2024

The growing popularity of text-as-data in various domain-specific applications and research has often relied on manually selected keywords or annotations. Although labor-intensive, expensive and time-consuming, the effectiveness of these efforts is not always guaranteed, especially in the early stages of research. This predicament raises the question of the extent to which large language models (LLMs) can aid in verifying the potential of a nascent research idea. This paper seeks to explore the reliability of LLM-suggested keywords in the automatic construction of the Economic Policy Uncertainty (EPU) index. Our findings confirm that LLMs can effectively automate the construction of EPU index. Furthermore, we delve into the potential of LLMs in enhancing the indicator construction process.

Recommended citation: Hsiu-Hsuan Yeh, Yu-Lieh Huang, Ziho Park, and Chung-Chi Chen. 2024. Automation of Text-Based Economic Indicator Construction: A Pilot Exploration on Economic Policy Uncertainty Index. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM '24).
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Introduction to Classical ML and Its Application in Econometric Research

Published: June 14, 2023

In this talk, I first introduce several basic concepts related to classical machine learning algorithms, such as overfitting, regularization, and adaptive boosting. I also showcase how to implement adaptive boosting in the Julia programming language to provide a more concrete understanding of how it works. Then, I move on to introduce a labor economics paper that utilizes machine learning algorithms to predict the treatment and control groups of the minimum wage policy based on demographic characteristics. Finally, I demonstrate how I employ this two-stage framework, which uses various machine learning algorithms to predict treatment and control groups used in the next stage of causal inference on Taiwanese data.

Introduction to Utilizing LLMs in Research

Published: January 26, 2024

This talk aims to share my experience in constructing prompts to instruct LLMs. The topics include, but are not limited to: common prompting strategies, popular LLMs that are not only used for conducting research but also serve as the backend technology for software, such as chatbots, code debugging agents, data science analytics, comparsion between prompting and fine-tuning, and more. Additionally, I briefly demonstrate how I leverage the prevailing LangChain Python API to implement the Few-Shot Chain-of-Thought prompting.

Louvain Method

Published: May 28, 2024

The Louvain Method is one of the most widely adopted community detection algorithms. Its popularity stems from its efficiency and ease of comprehension. Despite being intuitive, most of the technical details are missing in the original paper. Therefore, the goal of this presentation is to derive all relevant formulas, such as the modularity gain for a node moving out of the old community, moving into the new community, and the overall modularity gain for a single inter-community move for a node. Moreover, I conclude this presentation by conveying the intuition for a positive modularity gain movement.

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.