7 papers
COMPASS: A Framework for Evaluating Organization-Specific Policy Alignment in LLMs
Dasol Choi, DongGeon Lee, Brigitta Jesica Kartono +6
As large language models are deployed in high-stakes enterprise applications, from healthcare to finance, ensuring adherence to organization-specific policies has become essential.…
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…
When Good Sounds Go Adversarial: Jailbreaking Audio-Language Models with Benign Inputs
Hiskias Dingeto, Taeyoun Kwon, Dasol Choi +5
As large language models (LLMs) become increasingly integrated into daily life, audio has emerged as a key interface for human-AI interaction. However, this convenience also introd…
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…
Typed-RAG: Type-Aware Decomposition of Non-Factoid Questions for Retrieval-Augmented Generation
DongGeon Lee, Ahjeong Park, Hyeri Lee +2
Addressing non-factoid question answering (NFQA) remains challenging due to its open-ended nature, diverse user intents, and need for multi-aspect reasoning. These characteristics…
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…