1 citations · 1 across the 10 of their papers we have counts for
13 papers
SensorPersona: An LLM-Empowered System for Continual Persona Extraction from Longitudinal Mobile Sensor Streams
Bufang Yang, Lilin Xu, Yixuan Li +3
Personalization is essential for Large Language Model (LLM)-based agents to adapt to users' preferences and improve response quality and task performance. However, most existing ap…
ProAgent: Harnessing On-Demand Sensory Contexts for Proactive LLM Agent Systems in the Wild
Bufang Yang, Lilin Xu, Liekang Zeng +8
Recent studies have begun to explore proactive large language model (LLM) agents that provide unobtrusive assistance by automatically leveraging contextual information, such as in…
Clean First, Align Later: Benchmarking Preference Data Cleaning for Reliable LLM Alignment
Samuel Yeh, Sharon Li
Human feedback plays a pivotal role in aligning large language models (LLMs) with human preferences. However, such feedback is often noisy or inconsistent, which can degrade the qu…
Cognition-of-Thought Elicits Social-Aligned Reasoning in Large Language Models
Xuanming Zhang, Yuxuan Chen, Samuel Yeh +1
Large language models (LLMs) excel at complex reasoning but can still exhibit harmful behaviors. Current alignment strategies typically embed safety into model weights, making thes…
LH-Deception: Simulating and Understanding LLM Deceptive Behaviors in Long-Horizon Interactions
Yang Xu, Xuanming Zhang, Samuel Yeh +4
Deception is a pervasive feature of human communication and an emerging concern in large language models (LLMs). While recent studies document instances of LLM deception, most eval…
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…
LUMINA: Detecting Hallucinations in RAG System with Context-Knowledge Signals
Samuel Yeh, Sharon Li, Tanwi Mallick
Retrieval-Augmented Generation (RAG) aims to mitigate hallucinations in large language models (LLMs) by grounding responses in retrieved documents. Yet, RAG-based LLMs still halluc…
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…
Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations
Jinyuan Luo, Zhen Fang, Yixuan Li +2
Hallucination remains a key obstacle to the reliable deployment of large language models (LLMs) in real-world question answering tasks. A widely adopted strategy to detect hallucin…
MetaMind: Modeling Human Social Thoughts with Metacognitive Multi-Agent Systems
Xuanming Zhang, Yuxuan Chen, Samuel Yeh +1
Human social interactions depend on the ability to infer others' unspoken intentions, emotions, and beliefs-a cognitive skill grounded in the psychological concept of Theory of Min…
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…
Steer LLM Latents for Hallucination Detection
Seongheon Park, Xuefeng Du, Min-Hsuan Yeh +2
Hallucinations in LLMs pose a significant concern to their safe deployment in real-world applications. Recent approaches have leveraged the latent space of LLMs for hallucination d…
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…