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5 papers
Ideological Bias in LLMs' Economic Causal Reasoning
Donggyu Lee, Hyeok Yun, Jungwon Kim +4
Do large language models (LLMs) exhibit systematic ideological bias when reasoning about economic causal effects? As LLMs are increasingly used in policy analysis and economic repo…
EconCausal: A Context-Aware Economic Reasoning Benchmark for Large Language Models
Donggyu Lee, Hyeok Yun, Meeyoung Cha +3
Socio-economic causal effects depend heavily on their institutional and environmental contexts. The same intervention can produce different, even opposite, effects across regulator…
GeoReg: Weight-Constrained Few-Shot Regression for Socio-Economic Estimation using LLM
Kyeongjin Ahn, Sungwon Han, Seungeon Lee +6
Socio-economic indicators like regional GDP, population, and education levels, are crucial to shaping policy decisions and fostering sustainable development. This research introduc…
GeoSEE: Regional Socio-Economic Estimation With a Large Language Model
Sungwon Han, Donghyun Ahn, Seungeon Lee +5
Moving beyond traditional surveys, combining heterogeneous data sources with AI-driven inference models brings new opportunities to measure socio-economic conditions, such as pover…
Generalizable Disaster Damage Assessment via Change Detection with Vision Foundation Model
Kyeongjin Ahn, Sungwon Han, Sungwon Park +3
The increasing frequency and intensity of natural disasters call for rapid and accurate damage assessment. In response, disaster benchmark datasets from high-resolution satellite i…