Large Language Models (LLMs) are widely used for writing economic analysis reports or providing financial advice, but their ability to understand economic knowledge and reason about potential results of specific economic events lacks systematic evaluation. To address this gap, we propose a new dataset, natural language inference on economic events (EconNLI), to evaluate LLMs' knowledge and reasoning abilities in the economic domain. We evaluate LLMs on (1) their ability to correctly classify whether a premise event will cause a hypothesis event and (2) their ability to generate reasonable events resulting from a given premise. Our experiments reveal that LLMs are not sophisticated in economic reasoning and may generate wrong or hallucinated answers. Our study raises awareness of the limitations of using LLMs for critical decision-making involving economic reasoning and analysis. The dataset and codes are available at https://github.com/Irenehere/EconNLI.
Where Should I Study? Biased Language Models Decide! Evaluating Fairness in LMs for Academic Recommendations
Centre for Responsible AI (CeRAI), Wadhwani School of Data Science and AI (WSAI)（负责任人工智能中心（CeRAI）、Wadhwani数据科学与人工智能学校（WSAI））；Indian Institute of Technology Madras（印度理工学院马德拉斯学院）
EffiReason-Bench: A Unified Benchmark for Evaluating and Advancing Efficient Reasoning in Large Language Models
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Nanyang Technological University（南洋理工大学）；The Hong Kong University of Science and Technology（香港科技大学）；Singapore Institute of Manufacturing Technology, A*STAR（新加坡制造技术研究所，A*STAR）
Reliable Decision Support with LLMs: A Framework for Evaluating Consistency in Binary Text Classification Applications
Farmer School of Business, Miami University, Oxford, OH 45056, USA（迈阿密大学法默商学院）；College of Arts and Sciences, University of Dayton, Dayton, OH 45469, USA（Dayton大学文理学院）；Amsterdam Business School, University of Amsterdam, Amsterdam, The Netherlands（阿姆斯特丹大学阿姆斯特丹商学院）
