From the 1 of 7 linked papers with an AI index.
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Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures
Tyler A. Chang, Catherine Arnett, Abdelrahman Sadallah +377
To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we pre…
MAP4TS: A Multi-Aspect Prompting Framework for Time-Series Forecasting with Large Language Models
Suchan Lee, Jihoon Choi, Sohyeon Lee +4
Recent advances have investigated the use of pretrained large language models (LLMs) for time-series forecasting by aligning numerical inputs with LLM embedding spaces. However, ex…
Everyday Physics in Korean Contexts: A Culturally Grounded Physical Reasoning Benchmark
Jihae Jeong, DaeYeop Lee, DongGeon Lee +1
Existing physical commonsense reasoning benchmarks predominantly focus on Western contexts, overlooking cultural variations in physical problem-solving. To address this gap, we int…
Are Vision-Language Models Safe in the Wild? A Meme-Based Benchmark Study
DongGeon Lee, Joonwon Jang, Jihae Jeong +1
Rapid deployment of vision-language models (VLMs) magnifies safety risks, yet most evaluations rely on artificial images. This study asks: How safe are current VLMs when confronted…
REFIND at SemEval-2025 Task 3: Retrieval-Augmented Factuality Hallucination Detection in Large Language Models
DongGeon Lee, Hwanjo Yu
Hallucinations in large language model (LLM) outputs severely limit their reliability in knowledge-intensive tasks such as question answering. To address this challenge, we introdu…