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…
ERA: Evidence-based Reliability Alignment for Honest Retrieval-Augmented Generation
Sunguk Shin, Meeyoung Cha, Byung-Jun Lee +1
Retrieval-Augmented Generation (RAG) grounds language models in factual evidence but introduces critical challenges regarding knowledge conflicts between internalized parameters an…
Generalizable Slum Detection from Satellite Imagery with Mixture-of-Experts
Sumin Lee, Sungwon Park, Jeasurk Yang +2
Satellite-based slum segmentation holds significant promise in generating global estimates of urban poverty. However, the morphological heterogeneity of informal settlements presen…
Learning Multidimensional Urban Poverty Representation with Satellite Imagery
Sungwon Park, Sumin Lee, Jihee Kim +4
Recent advances in deep learning have enabled the inference of urban socioeconomic characteristics from satellite imagery. However, models relying solely on urbanization traits oft…