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20232026
most citedFactuality Challenges in the Era of Large Language Models

163 citations · 167 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SI2026

Mapping Emerging Climate Misinformation Playbooks in the Global South

Marcelo Sartori Locatelli, Wenchao Dong, Pedro Loures Alzamora +4

Climate misinformation continues to erode support for climate action, a challenge that is especially acute in the Global South, where high climate vulnerability intersects with dev…

cs.CL2025

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…

cs.CL2024

Persona Setting Pitfall: Persistent Outgroup Biases in Large Language Models Arising from Social Identity Adoption

Wenchao Dong, Assem Zhunis, Dongyoung Jeong +3

Drawing parallels between human cognition and artificial intelligence, we explored how large language models (LLMs) internalize identities imposed by targeted prompts. Informed by…

cs.CL2024★ 4 cited

I Am Not Them: Fluid Identities and Persistent Out-group Bias in Large Language Models

Wenchao Dong, Assem Zhunis, Hyojin Chin +2

We explored cultural biases-individualism vs. collectivism-in ChatGPT across three Western languages (i.e., English, German, and French) and three Eastern languages (i.e., Chinese,…

cs.CL2023★ 163 cited

Factuality Challenges in the Era of Large Language Models

Isabelle Augenstein, Timothy Baldwin, Meeyoung Cha +15

The emergence of tools based on Large Language Models (LLMs), such as OpenAI's ChatGPT, Microsoft's Bing Chat, and Google's Bard, has garnered immense public attention. These incre…