9 papers
GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalization
Pengyue Jia, Seongheon Park, Song Gao +2
Worldwide image geolocalization-the task of predicting GPS coordinates from images taken anywhere on Earth-poses a fundamental challenge due to the vast diversity in visual content…
VAUQ: Vision-Aware Uncertainty Quantification for LVLM Self-Evaluation
Seongheon Park, Changdae Oh, Hyeong Kyu Choi +2
Large Vision-Language Models (LVLMs) frequently hallucinate, limiting their safe deployment in real-world applications. Existing LLM self-evaluation methods rely on a model's abili…
Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities
Changdae Oh, Seongheon Park, To Eun Kim +8
Uncertainty quantification (UQ) for large language models (LLMs) is a key building block for safety guardrails of daily LLM applications. Yet, even as LLM agents are increasingly d…
Understanding Language Prior of LVLMs by Contrasting Chain-of-Embedding
Lin Long, Changdae Oh, Seongheon Park +1
Large vision-language models (LVLMs) achieve strong performance on multimodal tasks, yet they often default to their language prior (LP) -- memorized textual patterns from pre-trai…
GLSim: Detecting Object Hallucinations in LVLMs via Global-Local Similarity
Seongheon Park, Sharon Li
Object hallucination in large vision-language models presents a significant challenge to their safe deployment in real-world applications. Recent works have proposed object-level h…
HalluEntity: Benchmarking and Understanding Entity-Level Hallucination Detection
Min-Hsuan Yeh, Max Kamachee, Seongheon Park +1
To mitigate the impact of hallucination nature of LLMs, many studies propose detecting hallucinated generation through uncertainty estimation. However, these approaches predominant…